CN102279973A - Sea-sky-line detection method based on high gradient key points - Google Patents
Sea-sky-line detection method based on high gradient key points Download PDFInfo
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Abstract
The invention discloses a sea-sky-line detection method based on high gradient key points, and the method is suitable for ocean ship object identification and positioning devices. The method is characterized in that: based on a high efficiency attention mechanism of visual information, obtaining a statistical set of high gradient key points of an image column through employing a recursion optimization algorithm and a variable resolution sampling technique, subjecting the statistical set to least squares straight line fitting, rejecting outliers which do no satisfy a distance threshold condition in the statistical set and obtaining a selected set of the high gradient key points, subjecting the selected set to least squares straight line fitting, determining a selected set of the high gradient key points which satisfies an adaptive quantity threshold and a linear correlation threshold simultaneously as sea-sky-line. A problem of effective, accurate and real-time detection of sea-sky-line in a complex sea surface environment is solved by the sea-sky-line detection method based on high gradient key points, and the method has the characteristics of strong anti-interference capability and high calculating efficiency.
Description
Technical field
The invention belongs to Flame Image Process and computer vision field, relate generally to a kind of naval target image-recognizing method, relate in particular to a kind of sea horizon detection method that is used for sea target recognition and location.
Background technology
China has long shore line and wide marine territory, on the modern history of China, we " have the sea nothing to prevent ", but in today of 21st century, there is the present situation that falls behind in China naval informationization technology, still cause us " to have the perils of the sea to prevent ", can not adapt to the needs of the sacred marine territory of defendance China under the modernized information war.Under this background, research naval target image recognition technology realizes that the advanced information processing technology is pressing for of following high-tech war.In the military field of ocean, naval target is the important military target, it also is the main object of scouting and hitting, and remote naval target all is to appear near the sea horizon usually, target can be carried out potential zone location by detecting sea horizon, dwindle the scope of target search, reduce the calculated amount of Target Recognition, therefore the sea horizon detection is the prerequisite and the key of sea target recognition, and the research of sea horizon detection technique has very important military significance.
Look squarely at a distance under the state on the sea, sea Ship Target imaging generally is divided into three zones: day dummy section, sea horizon zone, zone, sea.If target occurs at a distance, then necessarily be in the sea horizon zone.Therefore in order to determine the sea horizon zone,, can determine the position that target occurs by detecting sea horizon.At These characteristics, by determining the sea horizon zone, for the calculated amount of work such as target detection that reduces follow-up air-sea background image and location, suppress noise unnecessary outside the sea horizon zone and the interference of decoy has great importance.
Sea and sky junction are the bigger zones of grey scale change, thereby sea horizon is that shade of gray changes more a little bigger line.Because sea horizon is a brightness by the gradation zone of high (sky) to low (sea) generally speaking, and all has certain degree of tilt usually, thus can be with its refinement, fit to straight line.
Have many scholars to carry out the research of sea horizon detection method in recent years, but traditional algorithm mainly adopt image partition method to detect.Chinese periodical " applicating technology " 2006, Vol.33, No.6, pp.96-98 has published one piece and has been entitled as the paper of " the detection algorithm research of complicated sea horizon zone ", people such as author Xie Hong disclose it and have adopted maximum variance between clusters (OTSU) to carry out the achievement in research that sea horizon detects in this paper, Fig. 1 has provided the experimental result that adopts this detection method.Fig. 1 (a) is an original image, and Fig. 1 (b) is a maximum variance between clusters OSTU Threshold Segmentation image, and Fig. 1 (c) is a stack OSTU cut-off rule on original image.As seen from Figure 1, it is unsatisfactory to adopt maximum variance between clusters to carry out the detection of sea horizon, this is because it is a kind of threshold segmentation method of integral body, and it adopts single threshold value to carry out image segmentation by the intensity profile characteristic of entire image, and this has determined that its anti-noise ability is relatively poor.Adopt the threshold value of an integral body to cut apart to entire image, beyond thought result can occur under many circumstances, especially may make part sea horizon deviation serious, serious deviation just occurred as the right half part on Fig. 1 (c).Owing to after adopting maximum variance between clusters to carry out Threshold Segmentation, be difficult to detect desirable sea horizon.Therefore, realize that effective sea horizon detects necessary research and seeks more effective, accurate and real-time algorithm.
Summary of the invention
The technical problem to be solved in the present invention is, overcomes the deficiencies in the prior art, propose a kind of can be in bad border of complicated sea sea horizon detection method effective, accurate, real-time, that antijamming capability is strong.
Sea horizon detection method of the present invention may further comprise the steps:
The first step after receiving the sense command that marine Ship Target is discerned and locating device sends, is the image of W*H by corresponding sensor acquisition one frame resolution;
In second step, ask for that w is listed as pairing high gradient key point p in the two field picture
iAnd form high gradient key point statistics and gather P={p
1, p
2..., p
w, wherein: high gradient key point p
iPosition in image is (x
i, y
i), i=1,2 ..., w, and w≤W, described high gradient key point p
iAt same adjacent two the line segment L that list
1And L
2Between have the poor of maximum gradation value mean value, described adjacent two line segment L
1And L
2Respectively contain M pixel, and M is much smaller than H;
In the 3rd step, calculate described high gradient key point statistics set P={p according to following formula
1, p
2..., p
wThe correlation parameter of least square fitting straight line:
In the formula, k, b are respectively the slopes of fitting a straight line y (x)=kx+b and cut square;
In the 4th step, calculate each high gradient key point P in the set of described high gradient key point statistics according to following formula
iFrom the fitting a straight line y of institute (x)=kx+b apart from dist:
The 5th step is with each the high gradient key point p that obtains
iApart from comparing of fitting a straight line, with the high gradient key point p that satisfies dist<D apart from dist and distance threshold D
iForm the selected set of high gradient key point P={p
1, p
2..., p
n, p wherein
i=(x
i, y
i), i=1,2 ..., n, and n≤w;
In the 6th step, judge whether the quantity n of the high gradient key point in the selected set of described high gradient key point satisfies self-adaptation amount threshold N, i.e. n>N, if yes, carried out for the 7th step,, then export original image and forwarded for the tenth step to locating device to marine Ship Target identification if not;
In the 7th step, calculate the selected set of described high gradient key point P={p according to following formula
1, p
2..., p
nThe correlation parameter of least square fitting straight line:
Wherein:
In the formula, k
1, b
1Be respectively fitting a straight line y (x)=k
1X+b
1Slope and cut a square;
In the 8th step, calculate the selected set of described high gradient key point P={p according to following formula
1, p
2..., p
nThe linearly dependent coefficient r of least square fitting straight line:
Wherein,
Linearly dependent coefficient r and linear dependence threshold value R are compared, if r>R judges the selected set of high gradient key point P={p
1, p
2..., p
nBe detected sea horizon and carry out next step, otherwise to marine Ship Target identification with locating device output original image and forwarded for the tenth step to;
In the 9th step, on a described two field picture, mark and draw sea horizon, and export this two field picture with locating device to marine Ship Target identification with straight line;
The tenth step, judge to have or not shutdown command, if not, then repeat nine steps of the first step to the, if yes, detection of end.
According to the present invention, ask for described high gradient key point p
iConcrete steps be: two adjacent segments L that calculate H pixel in the i row
1And L
2Gray-scale value mean value poor, calculate first and adopt following formula:
In the formula, f (L
1(j)), f (L
2(j)) be respectively the 1st adjacent two the line segment L of pixel
1And L
2On pixel L
1(j) and L
2(j) corresponding gray scale value; d
1Be the 1st adjacent two the line segment L of pixel
1And L
2Gray-scale value mean value poor; Calculate for the second time and later on and adopt following formula:
In the formula, f
NewBe j adjacent two the line segment L of pixel
1And L
2After pixel of picture altitude direction translation, increase gray values of pixel points newly, f
OldBe j adjacent two the line segment L of pixel
1And L
2After pixel of picture altitude direction translation, reject gray values of pixel points; d
jBe j adjacent two the line segment L of pixel
1And L
2Between gray-scale value mean value poor;
After the whole calculating of H pixel finish, adopt following formula to find the solution:
d
max=max(d
j),j=1,2,…,H
d
MaxPoor for this row maximum gradation value mean value, its pairing pixel position p
Max(x
Max, y
Max) be the high gradient key point position of these row in the image.
According to the present invention, as described high gradient key point statistics set P={p
1, p
2..., p
wIn high gradient key point p
iBe evenly distributed on the width of a described two field picture, and w=W/m.
According to the present invention, getting described distance threshold D is 5% of a described two field picture height H; Described self-adaptation amount threshold N is 30%~60% of w; Described linear dependence threshold value R is between 0.8~0.9.
Overall technology effect of the present invention is embodied in the following aspects.
(1) the present invention is based on the efficient attention mechanism of visual information, at first seek the statistics set of the high gradient key point in the two field picture row, and the least square line match is carried out in set to high gradient key point statistics.Do not satisfy after the point not in the know of distance threshold condition by rejecting, to obtain the selected set of high gradient key point.After the more selected set of high gradient key point being carried out the least square line match again, the selected set of high gradient key point of satisfying self-adaptation amount threshold and linear dependence threshold value simultaneously is judged to be sea horizon, in this two field picture, detected sea horizon is marked and drawed and exported to marine Ship Target with image and discern and locating device.Because detection method of the present invention meets the natural current conditions in day environment of sea, thereby, comparing with other existing sea horizon detection method, the present invention has precision height, characteristics that real-time is good.
(2) in the present invention, the statistics set of high gradient key point is to adopt the recursion optimized Algorithm, its basic way is, each of high gradient key point lists in asking for image, pixel gray-scale value to two adjacent line segments of each point is not to carry out whole adding up, but contrast grey value difference on adjacent two line segments according to the principle of translation from the top down, remove gray values of pixel points after adding newly-increased gray values of pixel points and deducting translation.This recursion optimized Algorithm has been avoided the needed huge calculated amount of method of exhaustion, has reduced calculation cost of the present invention to a great extent.
(3) in the present invention, the quantity of high gradient key point is based on that the variable resolution Sampling techniques determine in the set of the statistics of high gradient key point, promptly the horizontal direction resolution in the two field picture is reduced, not only saved computing time, and avoided around the sea horizon sea-surface target the interference of detection algorithm.Like this, even image is very big, handle back sea horizon detection algorithm through variable resolution and in the requirement of calculated amount, do not have much changes, and the uniform sampling method of this variable resolution can't the effect characteristics statistical accuracy, improved counting yield on the contrary to a great extent, and this is vital for real-time computer vision system.
(4) in the present invention, the self-adaptation amount threshold is set to the number percent of contained high gradient key point quantity in the set of high gradient key point statistics, this self-adaptation amount threshold that will judge the selected set of high gradient key point is arranged to the method that adapts with high gradient key point statistical value, greatly increased the adaptive ability and the flexibility ratio of sea horizon detection algorithm of the present invention, reduced the dependence of sea horizon detection algorithm image size and high gradient key point statistics number.
Description of drawings
Fig. 1 is the sea horizon test experience result of existing maximum variance between clusters image segmentation.
Fig. 2 is the operational flowchart of sea horizon detection method of the present invention.
The high gradient key point of optimizing based on recursion among Fig. 3 the present invention is extracted synoptic diagram.
Fig. 4 selects synoptic diagram based on the high gradient key point of variable resolution sampling among the present invention.
Fig. 5 is based on the least square method sea horizon test experience result of a plurality of high gradient key points.
Embodiment
The present invention is described in further detail below in conjunction with accompanying drawing and preferred embodiment.
Sea horizon shows as a statistical significance epigraph gray-scale value line jumpy in image, point on the sea horizon is normally perpendicular to changing maximum point on the gradation of image primary system meter meaning in the row pixel of sea horizon, these points are exactly the high gradient pixel in the image, are referred to as the high gradient key point in the present invention.Under a lot of situations, when target is positioned on the sea horizon or near the sea horizon time, relevant position shade of gray in the horizontal direction will significantly become greatly, also variation to some extent of shade of gray shows as the upper and lower edge of target, and no longer is sea horizon on the vertical direction.These variations cause the rectilinearity of sea horizon destroyed.Equally, the existence of large tracts of land cloud cluster detects influence obviously to sea horizon, because cloud cluster also is the violent place of grey scale change, easily makes detected sea horizon deflection sky.In addition, complicated sea clutter is also obscured the edge of wave of the detection, particularly high gradient of sea horizon to some extent, can make detected sea horizon dislocation.
The sea horizon detection method that the preferred embodiment of the present invention provides detects the software package realization by storer, image pick-up card and sea horizon are housed.Distance threshold D, self-adaptation amount threshold N and linear dependence threshold value R have been deposited in the storer.The function that sea horizon detects software package is, finishes the real-time detection of sea horizon according to workflow shown in Figure 2, and its testing process comprises following three parts content.
One, obtains the statistics set of high gradient key point
On computers behind the electricity, after receiving the sense command that marine Ship Target is discerned and locating device provides, sea horizon detects software package, and at first to obtain the frame resolution that respective sensor collects by image pick-up card be the image of W*H, and adopt the recursion optimized Algorithm and calculate high gradient key point statistics set P={p in this two field picture based on the variable resolution Sampling techniques
1, p
2..., p
wAnd i=1,2 ..., w, high gradient key point p
iPosition in image is (x
i, y
i).
Because The noise in optical imagery characteristic and the image, the gray-value variation between adjacent two pixels exists very big randomness.In order to improve these high gradient key points p
iThe accuracy and the reliability that detect, the present invention lists two adjacent segments L by calculating certain pixel same
1And L
2Gray-scale value mean value determine high gradient key point (referring to Fig. 3) in these row.Find the solution high gradient key point p
iThe specific implementation step be, list at the i of a two field picture, be that the center is provided with two adjacent line segment L along the picture altitude direction with first pixel
1And L
2, they all respectively comprise M pixel.Wherein, M is taken as between 5~12, if the value of M is too small, what then may detect is noise spot; If the value of M is excessive, then may detect less than the high gradient key point.In this preferred embodiment, get M=8.Calculate adjacent two line segment L first
1And L
2Gray-scale value mean value adopt following formula:
(1) in the formula, f (L
1(j)), f (L
2(j)) be respectively the 1st adjacent two the line segment L of pixel
1And L
2On pixel L
1(j) and L
2(j) corresponding gray scale value; d
1Be the 1st adjacent two the line segment L of pixel
1And L
2Gray-scale value mean value poor.
Because the height of image is H, then each pixel all needs to carry out the calculating of above-mentioned formula in H pixel of the row of one in the image, and calculating each time needs to make 2 * M sub-addition, 1 subtraction and 1 division, and wherein additional calculation occupies main computing time.Then each additional calculation that lists H point is 2 * M * H time.In order to save computing time, because one lists adjacent two line segment L of certain pixel
1And L
2It is repetition that N-1 point arranged in the calculating of (1) formula of employing, and having only a pixel is the calculation level that need add again, therefore can adopt the recursion optimization to calculate, thereby avoid the huge calculated amount of method of exhaustion.The basic ideas of recursion optimized Algorithm are, when asking for each high gradient key point that lists, to the adjacent two line segment L of each pixel
1And L
2On the pixel gray-scale value be not to carry out whole adding up, but according to two adjacent segments from the top down the principle of a pixel of translation contrast adjacent two line segment L
1And L
2On difference, then for the second time and after calculate adjacent two line segment L
1And L
2Gray-scale value mean value adopt following formula:
(2) in the formula, f
NewBe adjacent two line segment L
1And L
2After pixel of picture altitude direction translation, increase gray values of pixel points newly, f
OldBe adjacent two line segment L
1And L
2After pixel of picture altitude direction translation, reject gray values of pixel points; d
jBe j adjacent two the line segment L of pixel
1And L
2Between gray-scale value mean value poor.
When one list H pixel all calculate finish after, then find the solution the difference d that this lists maximum gray-scale value mean value
Max:
d
max=max(d
j),j=1,2,…,H (3)
The difference d of the maximum average value of seeking out by (3) formula
MaxPairing pixel position p
Max(x
Max, y
Max) be the high gradient key point position of the row of j in the image.
In a two field picture, its each row can adopt the recursion optimization to obtain a high gradient key point, to be used to carry out the fitting a straight line of sea horizon.That is to say for the two field picture that picture traverse is W, just have W row need carry out above-mentioned recursion computation optimization, its calculated amount can be very big, and simultaneously, it is also very big also can to cause the following adopted least square method to carry out the calculated amount of fitting a straight line.For this reason, the present invention adopts the sampling method selection portion apportion based on variable resolution to carry out the calculating of high gradient key point, to reduce calculated amount, as shown in Figure 4.Its concrete performing step is the width W of a two field picture to be carried out five equilibrium with an interval m pixel obtain w point, the i.e. intersection point of horizontal direction solid line and vertical direction solid line among Fig. 3; So, w is exactly the quantity that needs to carry out the high gradient key point of characteristic statistics among the present invention, and in other words, the present invention just finds the solution the high gradient key point to the row employing recursion optimized Algorithm at w some place.Handle through this resolution decreasing, even image is very big, reducing after m times, the calculated amount that its sea horizon detects is compared with the calculated amount that the general pattern sea horizon detects does not have much changes, and the uniform sampling method of this variable resolution can't the effect characteristics statistical accuracy, improved counting yield on the contrary to a great extent, and this point is vital for real-time computer vision system.In this preferred embodiment, the width W of image=600 (pixel), the height H of image=440 (pixel); Get m=20, obtain w=W/m=30.
Two, obtain the selected set of high gradient key point
Statistics set P={p in the high gradient key point of above-mentioned acquisition
1, p
2..., p
wIn, some high gradient key point is to be formed by noise, these points can influence the accuracy of detection of sea horizon, therefore, need be rejected these points not in the know by the linear fit algorithm.
To w high gradient key point p
i(x
i, y
i) linear fit be actually the linear regression problem, can come these p of match with straight line y (x)=kx+b
j(x
i, y
i).
For this reason, calculate two parameters of this fitting a straight line earlier by following formula:
(4) in the formula, k, b are respectively the slopes of institute's fitting a straight line and cut square.
Then, calculate each high gradient key point p by following formula
i(x
i, y
i) from the fitting a straight line y of institute (x)=kx+b apart from dist:
With each high gradient key point p
iCoordinate position (x
i, y
i) replace in (5) formula (x y) just can obtain and the corresponding one group of distance value of each high gradient key point.
If some high gradient key point from fitting a straight line apart from dist greater than distance threshold D, then these points are to belong to noise, should be with it rejecting, because can influence the precision of the straight line parameter of being calculated more like this away from the point of straight line.If some high gradient key point from straight line apart from dist smaller or equal to distance threshold D, then keep these high gradient key points, and constitute a new set P={p who contains n high gradient key point
1, p
2..., p
n, p wherein
i=(x
i, y
i), i=1,2 ..., n.This new set is called the selected set of high gradient key point in the present invention.Usually, distance threshold D is taken as 5% of picture altitude, and in this preferred embodiment, distance threshold D gets 22.
Three, whether the selected set of judging the high gradient key point is sea horizon
Next, following analyzing and processing is carried out in the selected set of the high gradient key point that the present invention need obtain previous step, could judge whether this selected set is sea horizon.
At first, judge selected set P={p
1, p
2..., p
nIn the number of high gradient key point whether greater than self-adaptation amount threshold N, if greater than self-adaptation amount threshold N, then carry out following respective handling, otherwise, finish the processing of this two field picture, export original image and begin the collection and the processing of next frame image with locating device to marine Ship Target identification.
Because the present invention adopts the statistics of carrying out the high gradient key point based on the variable resolution Sampling techniques, therefore, judge that whether the number that satisfies distance threshold condition point is inequitable greater than some fixing threshold values.Self-adaptation amount threshold N of the present invention is that the number percent of the high gradient key point number w in gathering according to high gradient key point statistics comes self-adaptation to determine.Be N be set to w 30%~60%.If the value of N is too small, then these points might be noise spots, and the sea horizon of being tried to achieve can be unstable; If the value of N is excessive, then, may can not get needed sea horizon because the point that satisfies condition does not reach the N value.In this preferred embodiment, N is set to 40% of w.This self-adaptation amount threshold that will judge the selected set of high gradient key point is arranged to the method that adapts with high gradient key point statistical value, greatly increased the adaptive ability and the flexibility ratio of sea horizon detection algorithm of the present invention, reduced the dependence of sea horizon detection algorithm image size and high gradient key point statistics number.
Secondly, under the satisfied situation of the selected set of high gradient key point, to the high gradient key point P={p in the selected set of high gradient key point greater than self-adaptation amount threshold N
1, p
2..., p
nAgain fit to straight line y (x)=k
1X+b
1, promptly calculate the slope k of this fitting a straight line with following formula
1With a section square b
1:
Wherein:
Next, whether the linearly dependent coefficient r that judges the selected set of high gradient key point is greater than linear dependence threshold value R, if judged result is for being, think that then the selected set of high gradient key point is detected sea horizon, at this moment, mark and draw a two field picture that demonstrates sea horizon to marine Ship Target identification and locating device output with straight line, otherwise, this frame original image exported.After this carry out the collection and the processing of next frame image.
Selected set can go out straight line with least square fitting for the high gradient key point, but some high gradient key point is away from fitting a straight line (for example noise spot or briming), and some high gradient key point just approaches straight line very much, and this just needs a criterion.Related coefficient is that it can reflect the level of intimate of one group of data linear dependence, promptly is defined as to the criterion of the linear dependence degree of institute's fitting a straight line:
Wherein,
(7) in the formula, r is the linearly dependent coefficient of the selected set of high gradient key point.The r absolute value approaches 1 more, and the linear relationship of the selected set of expression high gradient key point is good more, and the r of linear relation is 1.Related coefficient approaches 0, and the linear relation of the selected set of expression high gradient key point is very poor, perhaps not is straight line.Therefore, after the fitting a straight line, to calculate related coefficient usually, be used for weighing the linear relation degree.If r is greater than certain linear dependence threshold value R, then the linear relationship of straight line is satisfied in the selected set of high gradient key point, and the straight line that simulates is more satisfactory, and wherein linear dependence threshold value R is set between 0.8~0.9.In this preferred embodiment, R is set to 0.85.
More than three partial contents are sea horizon testing processes that a two field picture is carried out, thereby in application process of the present invention, said process carries out repeatedly, finishes after marine Ship Target identification provides shutdown command with locating device.
Fig. 5 has provided and has adopted the present invention that image is carried out the experimental result that sea horizon detects, and wherein, the resolution of Fig. 5 (a) and Fig. 5 (b) is 600*440 (pixel), and the resolution of Fig. 5 (c) and Fig. 5 (d) is 320*240 (pixel).Black line among the figure is represented to carry out the resulting fitting a straight line of fitting a straight line with the statistics set of high gradient key point for the first time; White straight line among the figure is represented to carry out fitting a straight line again and a new fitting a straight line obtaining with the selected set of high gradient key point.Fig. 5 (a) and Fig. 5 (c) expression match for the first time and match for the second time are same straight lines, and straight line overlaps, and is w=n therefore, does not have the situation of point not in the know.The match second time of Fig. 4 (b) and Fig. 4 (d) expression match for the first time and eliminating point not in the know is not a same straight line, is w<n therefore, and the situation of point not in the know is arranged.Fig. 5 (a) is the sea horizon testing result at Fig. 1 (a) original image, and Fig. 5 (b), Fig. 5 (c) and Fig. 5 (d) are the sea horizon testing results at other image.As can be seen, the present invention is because employing based on the least square method sea horizon detection method of a plurality of high gradient key points, therefore has the higher detection precision.
Claims (5)
1. sea horizon detection method based on the high gradient key point, it is characterized in that: this method may further comprise the steps:
The first step after receiving the sense command that marine Ship Target is discerned and locating device sends, is the image of W*H by corresponding sensor acquisition one frame resolution;
In second step, ask for that w is listed as pairing high gradient key point p in the two field picture
iAnd form high gradient key point statistics and gather P={p
1, p
2..., p
w, wherein: high gradient key point p
iPosition in image is (x
i, y
i), i=1,2 ..., w, and w≤W, described high gradient key point p
iAt same adjacent two the line segment L that list
1And L
2Between have the poor of maximum gradation value mean value, described adjacent two line segment L
1And L
2Respectively contain M pixel, and M is much smaller than H;
In the 3rd step, calculate described high gradient key point statistics set P={p according to following formula
1, p
2..., p
wThe correlation parameter of least square fitting straight line:
In the formula, k, b are respectively the slopes of fitting a straight line y (x)=kx+b and cut square;
In the 4th step, calculate each high gradient key point p in the set of described high gradient key point statistics according to following formula
iFrom the fitting a straight line y of institute (x)=kx+b apart from dist:
The 5th step is with each the high gradient key point p that obtains
iApart from comparing of fitting a straight line, with the high gradient key point p that satisfies dist<D apart from dist and distance threshold D
iForm the selected set of high gradient key point P={p
1, p
2..., p
n, p wherein
i=(x
i, y
i), i=1,2 ..., n, and n≤w;
In the 6th step, judge whether the quantity n of the high gradient key point in the selected set of described high gradient key point satisfies self-adaptation amount threshold N, i.e. n>N, if yes, carried out for the 7th step,, then export original image and forwarded for the tenth step to locating device to marine Ship Target identification if not;
In the 7th step, calculate the selected set of described high gradient key point P={p according to following formula
1, p
2..., p
nThe correlation parameter of least square fitting straight line:
Wherein:
In the formula, k
1, b
1Be respectively fitting a straight line y (x)=k
1X+b
1Slope and cut a square;
In the 8th step, calculate the selected set of described high gradient key point P={p according to following formula
1, p
2..., p
nThe linearly dependent coefficient r of least square fitting straight line:
Wherein,
Linearly dependent coefficient r and linear dependence threshold value R are compared, if r>R judges the selected set of high gradient key point P={p
1, p
2..., p
nBe detected sea horizon and carry out next step, otherwise to marine Ship Target identification with locating device output original image and forwarded for the tenth step to;
In the 9th step, on a described two field picture, mark and draw sea horizon, and export this two field picture with locating device to marine Ship Target identification with straight line;
The tenth step, judge to have or not shutdown command, if not, then repeat nine steps of the first step to the, if yes, detection of end.
2. the sea horizon detection method based on the high gradient key point according to claim 1 is characterized in that: ask for described high gradient key point p
iConcrete steps be: two adjacent segments L that calculate H pixel in the i row
1And L
2Gray-scale value mean value poor, calculate first and adopt following formula:
In the formula, f (L
1(j)), f (L
2(j)) be respectively the 1st adjacent two the line segment L of pixel
1And L
2On pixel L
1(j) and L
2(j) corresponding gray scale value; d
1Be the 1st adjacent two the line segment L of pixel
1And L
2Gray-scale value mean value poor; Calculate for the second time and later on and adopt following formula:
In the formula, f
NewBe j adjacent two the line segment L of pixel
1And L
2After pixel of picture altitude direction translation, increase gray values of pixel points newly, f
OldBe j adjacent two the line segment L of pixel
1And L
2After pixel of picture altitude direction translation, reject gray values of pixel points; d
jBe j adjacent two the line segment L of pixel
1And L
2Between gray-scale value mean value poor;
After the whole calculating of H pixel finish, adopt following formula to find the solution:
d
max=max(d
j),j=1,2,…,H
d
MaxPoor for this row maximum gradation value mean value, its pairing pixel position p
Max(x
Max, y
Max) be the high gradient key point position of these row in the image.
3. the sea horizon detection method based on the high gradient key point according to claim 1 is characterized in that: as described high gradient key point statistics set P={p
1, p
2..., p
wIn high gradient key point p
iBe evenly distributed on the width of a described two field picture, and w=W/m.
4. according to claim 1 or 2 or 3 described sea horizon detection methods based on the high gradient key point, it is characterized in that: getting described distance threshold D is 5% of a described two field picture height H; Described self-adaptation amount threshold N is 30%~60% of w; Described linear dependence threshold value R is between 0.8~0.9.
5. the sea horizon detection method based on the high gradient key point according to claim 4 is characterized in that: the width W of a described two field picture=600 (pixel), height H=440 (pixel); Get m=20 (pixel), promptly calculate described high gradient key point p
iRow w=30; Get M=20, N=12, R=0.85.
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