CN108615028A - The fine granularity detection recognition method of harbour heavy vehicle - Google Patents
The fine granularity detection recognition method of harbour heavy vehicle Download PDFInfo
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- CN108615028A CN108615028A CN201810455259.3A CN201810455259A CN108615028A CN 108615028 A CN108615028 A CN 108615028A CN 201810455259 A CN201810455259 A CN 201810455259A CN 108615028 A CN108615028 A CN 108615028A
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- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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Abstract
The present invention relates to a kind of fine granularity detection recognition methods of harbour heavy vehicle, camera lens is installed on driving vehicle towards the high definition camera in front of vehicle traveling, high definition camera is connect with computing module, the imaging data of camera is read using computing module, the vehicle detection identification within the scope of field of front vision is carried out, steps are as follows for specific method:1) image-forming information of acquisition target port vehicle in the camera;2) response transform and merging response;3) the fine granularity detection of vehicle;4) influence that different vehicle posture brings detection is solved.Advantageous effect:The present invention is by high definition camera and computing module, for the harbour heavy vehicle difference imaging results under all kinds of scenes, can make fast and accurately detection identification.
Description
Technical field
The invention belongs to field of vehicle detection more particularly to a kind of fine granularity detection recognition methods of harbour heavy vehicle.
Background technology
Harbour is important land and water distribution centre, and harbour heavy vehicle assumes responsibility for what large cargo was converted between transportation by land and water
Key player.Traffic density is high under harbour service environment, and compared with middle-size and small-size vehicle, the driving difficulty of heavy vehicle is more
Greatly, this just to driver, more stringent requirements are proposed.Therefore, research and development are put on for the auxiliary driving technology of harbour heavy vehicle
Schedule, and wherein for the detection of harbour heavy vehicle and identification it is basis and the core of the technology.The main vehicle class at harbour
It Wei not the uncommon vehicle of real roads such as truck (including tractor and vehicle hang), fork truck.Under harbour service environment, different appearances
There are larger differences for the imaging of vehicle in the camera under state, and harbour truck has the short vehicle of carrying and hangs, long vehicle extension, loaded collection
Vanning, unloaded container, all kinds of different state scene compositions such as loading container.
Current vehicle detection identifying schemes some by means of expensive laser radar apparatus, though however laser radar
Barrier can so be effectively detected out, but its collected information density is small, it is highly difficult to extract target signature, therefore only
It is difficult to identify vehicle in the environment by laser radar.It is some other using camera as the vehicle detection identification side of sensor
Case rarely has for harbour service environment, and it is even more impossible to make fine-grained detection to harbour heavy vehicle to identify, system is driven for auxiliary
The information for Decision Control provided of uniting is extremely limited.
Invention content
It is an object of the invention to overcome the defect of prior art, and provide a kind of detections of the fine granularity of harbour heavy vehicle to identify
Method carries out the fine-grained detection identification of vehicle, significantly carries for the harbour heavy vehicle difference imaging results under all kinds of scenes
The high detection recognition accuracy of harbour heavy vehicle.
The present invention to achieve the above object, is achieved through the following technical solutions, a kind of fine granularity inspection of harbour heavy vehicle
Detection identifying method, it is characterized in that:Installation camera lens is towards the high definition camera in front of vehicle traveling on driving vehicle, by high definition camera
It is connect with computing module, the imaging data of camera is read using computing module, the vehicle detection carried out within the scope of field of front vision is known
Not, steps are as follows for specific method:
1) image-forming information of acquisition target port vehicle in the camera
1. acquiring harbour service ambient image data, and the vehicle detection target in image is labeled, will be marked
Image sample data be used for model training, build multigroup filter template and the deformable component feature space model of target, extract
Spatial model and filter template;
2. extracting candidate window on the image marked using sliding window method, the direction gradient histogram of image is calculated
Figure feature, according to the global root filter of histograms of oriented gradients pyramid training and multiple partial models, wherein each component
Model all includes a component filter and a spatial model;The component filter for respond headstock, vehicle extension, wheel,
Container class vehicle assembly, spatial model define the relative position that each component detected occurs in global detection window,
The deformation loss brought for calculating unit change in location;
2) response transform and merging response
1. in detection process, for the image collected data, extracting its histograms of oriented gradients feature pyramid, calculate
Response between low resolution feature and root filter;
2. calculating the response between high-resolution features and all parts filter, size change over is done to response computation result
Afterwards, merge two-part response computation as a result, making comparisons with the spatial model that training obtains before, calculate setting for each window x
Confidence score, calculation formula are:
fβ=maxzβ Φ (x, z)
Wherein:
Z is the relative position that each component occurs in the window
β is filter and spatial model parameter;
3. a threshold value is arranged to the confidence level of window, the window that score is more than to threshold value is set as high credible of fitting degree
Testing result;
3) the fine granularity detection of vehicle
Different filter templates is built for the heavy vehicle of different scenes, each group that the same window uses is filtered
Device calculates response, and finding makes the highest filter group of window scores, and the corresponding scene of this group of filter is testing result,
Realize fine-grained detection identification;
4) influence that different vehicle posture brings detection is solved
It is imaged existing difference in the camera for vehicle difference posture, with the method for structure spatial model, vehicle
Different postures will cause the relative position that each component of vehicle occurs in detection window different, and spatial model contains vehicle each group
The various positions information that part is likely to occur in detection window can be fitted the position distribution situation of each unit response, and solution is never
The influence that detection is brought with vehicle attitude.
The high definition camera uses frame rate in 20fps or more, the imaging color camera more than 720P resolution ratio.
The computing module selects computing modules of the CPU more than dominant frequency 2.0GHz.It adjusts camera focus and is not less than 30 meters.
Advantageous effect:Compared with prior art, the present invention is by slr camera and computing module, independent of laser radar
Or other sensors, it is simple in structure, convenient for transplanting.For the harbour heavy vehicle difference imaging results under all kinds of scenes, equal energy
Fast and accurately detection identification is enough made, speed is not less than 20fps, the detection within the scope of 20 meters of camera visible angle to vehicle
Rate is not less than 90%.
Description of the drawings
Fig. 1 is the work block diagram of the present invention.
Fig. 2 is the scheme of installation of apparatus of the present invention.
A, camera model, B, computing module, C, camera fields of view, D, car body.
Specific implementation mode
Below in conjunction with preferred embodiment, to the specific implementation mode that provides according to the present invention, details are as follows:
Attached drawing 1 is referred to, present embodiment discloses a kind of fine granularity detection recognition methods of harbour heavy vehicle, are driving vehicle
High definition camera connect with computing module towards the high definition camera in vehicle traveling front, uses computing module by installation camera lens on
The imaging data of camera is read, carries out the vehicle detection identification within the scope of field of front vision, steps are as follows for specific method:
1) image-forming information of acquisition target port vehicle in the camera
1. acquiring harbour service ambient image data, and the vehicle detection target in image is labeled, will be marked
Image sample data be used for model training, build multigroup filter template and the deformable component feature space model of target, extract
Spatial model and filter template;
2. extracting candidate window on the image marked using sliding window method, the direction gradient histogram of image is calculated
Figure feature, according to the global root filter of histograms of oriented gradients pyramid training and multiple partial models, wherein each component
Model all includes a component filter and a spatial model;The component filter for respond headstock, vehicle extension, wheel,
Container class vehicle assembly, spatial model define the relative position that each component detected occurs in global detection window,
The deformation loss brought for calculating unit change in location;
2) response transform and merging response
1. in detection process, for the image collected data, extracting its histograms of oriented gradients feature pyramid, calculate
Response between low resolution feature and root filter;
2. calculating the response between high-resolution features and all parts filter, size change over is done to response computation result
Afterwards, merge two-part response computation as a result, making comparisons with the spatial model that training obtains before, calculate setting for each window x
Confidence score, calculation formula are:
fβ=maxzβ Φ (x, z)
Wherein:
Z is the relative position that each component occurs in the window
β is filter and spatial model parameter;
3. a threshold value is arranged to the confidence level of window, the window that score is more than to threshold value is set as high credible of fitting degree
Testing result;
3) the fine granularity detection of vehicle
Different filter templates is built for the heavy vehicle of different scenes, each group that the same window uses is filtered
Device calculates response, and finding makes the highest filter group of window scores, and the corresponding scene of this group of filter is testing result,
Realize fine-grained detection identification;
4) influence that different vehicle posture brings detection is solved
It is imaged existing difference in the camera for vehicle difference posture, with the method for structure spatial model, vehicle
Different postures will cause the relative position that each component of vehicle occurs in detection window different, and spatial model contains vehicle each group
The various positions information that part is likely to occur in detection window can be fitted the position distribution situation of each unit response, and solution is never
The influence that detection is brought with vehicle attitude.
The high definition camera uses frame rate in 20fps or more, the imaging color camera more than 720P resolution ratio.
The computing module selects computing modules of the CPU more than dominant frequency 2.0GHz.It adjusts camera focus and is not less than 30 meters.
Embodiment
Detection device is installed as shown in Figure 2, and camera model A is mounted at the top of car body D, and frame rate 20fps is differentiated
Rate is 720P, towards vehicle heading, adjusts camera fields of view C, and it is 30 meters to make its focal length, and is by itself and CPU frequency
The computing module B connections of 2.0GHz.Image data is acquired using camera, the image sample data marked is instructed for model
Practice.During model training, setting model learning rate is 0.001, and model parameter is carried out more using stochastic gradient descent method
Newly, loss function uses log-likelihood function, and adds L2 regular terms, and regularization coefficient is set as 0.05.It is carried after the completion of training
Take out model.After collecting the image data for detection, model is entered data into, model will export the result of vehicle detection.
Reliability of this method for vehicle detection identification is assessed using accuracy rate and recall rate.In front of high definition camera
Within the scope of 30 meters of the visual field, this method can effectively detect that 90% or more vehicle, probability of false detection are no more than 30%, that is, are ensureing
In the case that recall rate is not less than 90%, Detection accuracy is not less than 70%, can be provided with robust for automatic Pilot technology
The vehicle cognitive method of property, valuable decision information is provided for the decision process of automatic Pilot.
In implementation process, need to solve the influence that different vehicle posture brings detection.Since the different postures of vehicle will
Cause the relative position that each component of vehicle occurs in detection window different, using the method for structure spatial model, spatial model
The various positions information that each component of vehicle is likely to occur in detection window is contained, the position point of each unit response can be fitted
Cloth situation, to solve the influence that different vehicle posture brings detection.
The above-mentioned detailed description that a kind of fine granularity detection recognition method of the harbour heavy vehicle is carried out with reference to embodiment,
It is illustrative without being restrictive, several embodiments can be enumerated according to limited range, therefore do not departing from this hair
Change and modification under bright general plotting should belong within protection scope of the present invention.
Claims (3)
1. a kind of fine granularity detection recognition method of harbour heavy vehicle, it is characterized in that:Camera lens direction is installed on driving vehicle
The high definition camera in vehicle traveling front, high definition camera is connect with computing module, the imaging number of camera is read using computing module
According to the vehicle detection carried out within the scope of field of front vision identifies that steps are as follows for specific method:
1) image-forming information of acquisition target port vehicle in the camera
1. acquiring harbour service ambient image data, and the vehicle detection target in image is labeled, the figure that will have been marked
Decent notebook data is used for model training, builds multigroup filter template and the deformable component feature space model of target, extracts space
Model and filter template;
2. extracting candidate window on the image marked using sliding window method, the histograms of oriented gradients for calculating image is special
Sign, according to the global root filter of histograms of oriented gradients pyramid training and multiple partial models, wherein each partial model
All include a component filter and a spatial model;The component filter is for responding headstock, vehicle extension, wheel, container
Class vehicle assembly, spatial model define the relative position that each component detected occurs in global detection window, are used for
The deformation loss that calculating unit change in location is brought;
2) response transform and merging response
1. in detection process, for the image collected data, extracting its histograms of oriented gradients feature pyramid, calculating low point
Response between resolution feature and root filter;
2. the response between high-resolution features and all parts filter is calculated, after doing size change over to response computation result,
Merge two-part response computation as a result, making comparisons with the spatial model that training obtains before, calculates the confidence level of each window x
Score, calculation formula are:
fβ=maxzβ Φ (x, z)
Wherein:
Z is the relative position that each component occurs in the window
β is filter and spatial model parameter;
3. a threshold value is arranged in the confidence level to window, the window that score is more than to threshold value is set as the high credible detection of fitting degree
As a result;
3) the fine granularity detection of vehicle
Different filter templates is built for the heavy vehicle of different scenes, each group filter meter used the same window
Response is calculated, finding makes the highest filter group of window scores, and the corresponding scene of this group of filter is testing result, realizes
Fine-grained detection identification;
4) influence that different vehicle posture brings detection is solved
It is imaged existing difference in the camera for vehicle difference posture, with the method for structure spatial model, the difference of vehicle
Posture will cause the relative position that each component of vehicle occurs in detection window different, and spatial model contains each component of vehicle and exists
The various positions information being likely to occur in detection window can be fitted the position distribution situation of each unit response, solve different vehicles
The influence that posture brings detection.
2. the fine granularity detection recognition method of harbour heavy vehicle according to claim 1, it is characterized in that:The high definition phase
Machine uses the frame rate of 20fps or more, imaging color camera more than 720P resolution ratio.
3. the fine granularity detection recognition method of harbour heavy vehicle according to claim 1, it is characterized in that:The calculating mould
Block selects computing modules of the CPU more than dominant frequency 2.0GHz.It adjusts camera focus and is not less than 30 meters.
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