CN111127494A - Expressway truck weight limit identification method based on image processing - Google Patents
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- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000013507 mapping Methods 0.000 claims description 3
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Abstract
The invention discloses a method for identifying weight limits of express highway trucks based on image processing, which comprises the steps of obtaining truck photos of target trucks running on an express highway, preprocessing the truck photos to obtain corrected photos, carrying out edge detection on the corrected photos to determine edge points on the corrected images, identifying the number of target axles of the target trucks according to the edge points on the corrected images, calculating the axle distance ratio between the axles of the target trucks, and determining the weight limits of the target trucks according to the number of the target axles and the axle distance ratio.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for identifying weight limits of express highway trucks based on image processing.
Background
The weight limit identification is carried out on the trucks running on the expressway, and whether the corresponding trucks run in an overload state or not can be detected in time so as to prevent related accidents and ensure a safe and smooth running environment. Most of the existing vehicle identification technologies are used for processing vehicle pictures according to image morphology and matching a database, and most of the existing vehicle identification technologies are used for constructing corresponding model identification matching by using parameter characteristics (color, size, vehicle type and the like) of vehicle outlines so as to realize truck weight limit identification.
Disclosure of Invention
Aiming at the problems, the invention provides a method for identifying the weight limit of a highway truck based on image processing.
In order to realize the purpose of the invention, the invention provides a method for identifying the weight limit of a highway truck based on image processing, which comprises the following steps:
s10, acquiring a photo of a target truck running on the expressway, and preprocessing the photo of the truck to obtain a corrected photo;
s20, carrying out edge detection on the corrected picture, and determining edge points on the corrected picture;
s30, identifying the number of target axles of the target truck according to the edge points on the corrected image;
s40, calculating the wheelbase ratio among the axles of the target truck;
and S50, determining the weight limit of the target truck according to the target axle number and the axle-to-axle ratio.
In one embodiment, the preprocessing the wagon photos comprises:
graying the wagon photos by adopting a weighted average method principle, setting weights corresponding to various color components, carrying out weighted average on the grayed wagon photos, and correcting pixels which are not in a preset grayscale range in the wagon photos by adopting gamma verification.
In one embodiment, the edge detecting the modified photo comprises:
and adopting a Sobel edge detection operator to carry out edge detection on the corrected photo.
In one embodiment, the identifying the number of target axles of the target truck according to the edge points on the modified image comprises:
identifying edge points in the circular images in the corrected images, mapping the edge points in the circular images into a parameter space from an image space, searching the circle centers of all the circular images in the parameter space, and solving the radius coordinates of all the circular images so as to finally determine all initial circles in the corrected images;
determining a selection cluster according to each initial circle, taking a point which is farthest from the centroid of the corresponding selection cluster in each selection cluster as a first selection point, then sequentially selecting the point which is farthest from the selected selection point as the selection point until more than 10 selection points are selected, and determining a circle which takes the centroid as the center of a circle and passes through the selection point to draw out a plurality of selection circles;
contracting the selection circle to the corresponding selection cluster according to the preset circle parameter to obtain a contracted circle, determining the circle center of the contracted circle as a representative point, determining initial clustering data according to the contracted circle within the preset circle parameter range, circulating a CURE algorithm, clustering by adopting the mass center of each selection cluster and the distance of the representative point to obtain an axle circle representing an axle, and determining the target axle number of the target truck according to each axle circle.
In one embodiment, the determining the weight limit of the target truck according to the target axle number and the axle-to-axle ratio comprises:
obtaining an axis parameter-weight limit comparison table; the axle parameter-weight limit comparison table records the corresponding relation between the number of axles of the truck and the weight limit;
searching the weight limit corresponding to the target axis number in the axis parameter-weight limit comparison table;
and if a plurality of weight limits corresponding to the target axle number are obtained, determining the axle base ratio to the corresponding weight limit as the weight limit of the target truck.
According to the method for identifying the weight limit of the express way freight car based on the image processing, the freight car photo of the target freight car running on the express way is obtained, the freight car photo is preprocessed to obtain the corrected photo, the edge detection is carried out on the corrected photo to determine the edge point on the corrected image, the target axle number of the target freight car is identified according to the edge point on the corrected image, the axle distance ratio between the axles of the target freight car is calculated, the weight limit of the target freight car is determined according to the target axle number and the axle distance ratio, the cost of identifying the weight limit of the express way freight car can be greatly reduced, the influence brought in the installation aspect can be reduced, and the novel image identification intelligent technology can be utilized to process the weight limit of the target freight car, and compared with manual detection or machine detection, the method has the advantages of being efficient and easy to carry out.
Drawings
FIG. 1 is a flowchart of a method for identifying a weight limit of a highway truck based on image processing according to an embodiment;
FIG. 2 is a graph illustrating the effect of graying according to one embodiment;
FIG. 3 is a diagram of an embodiment after edge detection by the Sobel operator;
fig. 4 is a flowchart of a method for identifying a weight limit of a highway truck based on image processing according to another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a flowchart of a method for identifying a weight limit of a highway truck based on image processing according to an embodiment, and the method includes the following steps:
s10, acquiring a photo of a target truck running on the expressway, and preprocessing the photo of the truck to obtain a corrected photo;
the steps can obtain the photo of the truck of the target truck running on the expressway through a monitoring system arranged on the expressway, and then carry out preprocessing such as image graying, image correction and the like on the photo of the truck to obtain a corrected photo.
In one embodiment, the preprocessing the wagon photos comprises:
graying the wagon photos by adopting a weighted average method principle, setting weights corresponding to various color components, carrying out weighted average on the grayed wagon photos, and correcting pixels which are not in a preset grayscale range in the wagon photos by adopting gamma verification.
The preset gray scale range can be set according to the preprocessing precision. The preset gray scale range may include an upper gray scale value and a lower gray scale value, and the pixels having the gray scale value greater than the upper gray scale value or the gray scale value less than the lower gray scale value are pixels not in the preset gray scale range.
Specifically, the gray scale may be first grayed by using the weighted average method, corresponding weights are given to the color components according to different recognition sensitivities of human eyes to the color components, weighted average is then performed, and then gamma correction is used to correct an image with a too high gray value (too bright image) or too low gray value (too dark image), so as to increase the contrast of the feature to be recognized in the image, thereby improving the display effect of the image.
In one example, a graying process effect map can be seen with reference to fig. 2.
S20, carrying out edge detection on the corrected picture, and determining edge points on the corrected picture;
in one embodiment, the edge detecting the modified photo comprises:
and adopting a Sobel edge detection operator to carry out edge detection on the corrected photo.
And (5) performing edge detection by using a Sobel edge detection operator. Typically, the pixel values at the edges of the image change significantly, and one way to indicate this change is to use derivatives. When the change of the gradient value indicates that the content in the image also changes, for the purpose of explanation by using a more visual image, a one-dimensional graph is used, when the slope of a certain point of the graph tends to be infinite, the 'jump' of the gray value of the point indicates the existence of the edge, and the existence of the 'jump' of the edge can be more clearly seen by using first-order differential derivation.
In one example, the schematic diagram after edge detection of Sobel operator can refer to fig. 3.
S30, identifying the number of target axles of the target truck according to the edge points on the corrected image;
the above steps may be performed with respect to the edge points on the corrected image, so as to determine an axle circle representing the axle, thereby determining the number of target axles of the target truck.
S40, calculating the wheelbase ratio among the axles of the target truck;
and S50, determining the weight limit of the target truck according to the target axle number and the axle-to-axle ratio.
After the weight limit of the target truck is identified, whether the target truck is overloaded or not can be detected, so that the safe operation of the target truck is ensured.
According to the method for identifying the weight limit of the express way freight car based on the image processing, the freight car photo of the target freight car running on the express way is obtained, the freight car photo is preprocessed to obtain the corrected photo, the edge detection is carried out on the corrected photo to determine the edge point on the corrected image, the target axle number of the target freight car is identified according to the edge point on the corrected image, the axle distance ratio between the axles of the target freight car is calculated, the weight limit of the target freight car is determined according to the target axle number and the axle distance ratio, the cost of identifying the weight limit of the express way freight car can be greatly reduced, the influence brought in the installation aspect can be reduced, and the novel image identification intelligent technology can be utilized to process the weight limit of the target freight car, and compared with manual detection or machine detection, the method has the advantages of being efficient and easy to carry out.
In one embodiment, the identifying the number of target axles of the target truck according to the edge points on the modified image comprises:
identifying edge points in the circular images in the corrected images, mapping the edge points in the circular images into a parameter space from an image space, searching the circle centers of all the circular images in the parameter space, and solving the radius coordinates of all the circular images so as to finally determine all initial circles in the corrected images;
determining a selection cluster according to each initial circle, taking a point which is farthest from the centroid of the corresponding selection cluster in each selection cluster as a first selection point, then sequentially selecting the point which is farthest from the selected selection point as the selection point until more than 10 selection points are selected, and determining a circle which takes the centroid as the center of a circle and passes through the selection point to draw out a plurality of selection circles;
contracting the selection circle to the corresponding selection cluster according to the preset circle parameter to obtain a contracted circle, determining the circle center of the contracted circle as a representative point, determining initial clustering data according to the contracted circle within the preset circle parameter range, circulating a CURE algorithm, clustering by adopting the mass center of each selection cluster and the distance of the representative point to obtain an axle circle representing an axle, and determining the target axle number of the target truck according to each axle circle.
Specifically, in this embodiment, a Hough algorithm may be used to map edge points in a circular image from an image space to a parameter space (a, b, r) by using a correlation formula, and since the original digital image adopts polar coordinates and a parameter range of a certain number is set, points in the original image space may be mapped to the parameter space by multiple cycles, a circle center is found in the parameter space, a radius coordinate is obtained, and after the parameters of the circle are determined, each initial circle may be finally obtained.
Setting parameters of a circle on the basis of contour identification to determine preset circle parameters; then, using a CURE algorithm, selecting a point farthest from the centroid in each selected cluster as a first point, then sequentially selecting the points farthest from the selected points until more than 10 points are selected, drawing a circle which takes the centroid as the center of the circle and passes through the selected points to achieve the purpose of primary drawing, shrinking the cluster (selected cluster) captured by the points according to the parameters of the circle to obtain a shrunk representative point, and then screening out a circle or an ellipse which is not the representative point according to preset circle parameters (namely, a circle or an ellipse of which the parameters such as radius are not in the preset circle parameters) to achieve the effect of primary clustering; and (5) circulating the CURE algorithm again, clustering by using the distances between the centroid and the representative points, and finally obtaining a final circle for describing the axle to determine the number of the target axles of the target truck.
In one embodiment, the determining the weight limit of the target truck according to the target axle number and the axle-to-axle ratio comprises:
obtaining an axis parameter-weight limit comparison table; the axle parameter-weight limit comparison table records the corresponding relation between the number of axles of the truck and the weight limit;
searching the weight limit corresponding to the target axis number in the axis parameter-weight limit comparison table;
and if a plurality of weight limits corresponding to the target axle number are obtained, determining the axle base ratio to the corresponding weight limit as the weight limit of the target truck.
The axle parameter-weight limit comparison table records the corresponding relation between the number of axles of the truck and the weight limit, and if a plurality of weight limits correspond to a certain number of axles, the axle parameter-weight limit comparison table can also record the weight limits corresponding to the distance ratios of each (or each section of) axle of the truck with the number of axles.
The wheelbase ratio may be: the ratio of the distance from the center of mass of the first axis to the center of mass of the second axis to the distance from the center of mass of the second axis to the center of mass of the last axis.
Specifically, the axle parameter-weight limit table can be set according to various truck characteristics. For example, trucks of different models and manufacturers can be used for different sizes, shapes and wheelbase ratios, axle ratios and weight limits of the trucks are set differently, then the described center of mass of a circle of an axle is found, distance parameters are set, and when the truck is identified as 2 axles, the weight limit is directly output to 18 tons; when 3 axes are identified, comparing the distance from the center of mass of the first axis to the center of mass of the second axis with the distance from the center of mass of the second axis to the center of mass of the last axis, meeting the given proportion range, directly outputting 25 tons, and if the given proportion range is not met, outputting 27 tons; when 4 axles are identified, 31 tons and 36 tons are divided, a first axle and a second axle or a third axle and a fourth axle of the 31-ton truck are an axle group, and the standard distance is 1350mm, so whether the proportion of the distance between the second axle and the third axle and the distance between the third axle and the fourth axle or the proportion of the distance between the first axle and the second axle and the distance between the second axle and the third axle meets the proportion requirement is considered, if yes, 31 tons are output, and if not, 36 tons are output; when the axle is identified as 5 axles, the axles are divided into 42 tons and 43 tons, and because the axle distance ratio of 42 tons, the third axle, the fourth axle and the fifth axle are an axle group, and the standard distance is 1350mm, only the distance ratio between the first axle, the second axle and the third axle needs to be compared to determine whether the ratio meets the proportion requirement, 42 tons are output if the ratio meets the proportion requirement, and 43 tons are output if the ratio does not meet the proportion requirement; when identifying 6 axles, a weight limit of between 46 and 49 tons is directly identified.
In one example, the correspondence between the number of axles and the weight limit of each type of truck can be found in table 1.
TABLE 1
In one example, an axis parameter-weight limit ratio table may be referenced in table 2.
TABLE 2
In an embodiment, the method for identifying the weight limit of the express highway truck based on the image processing may also be as shown in fig. 4, and the truck photos are sequentially subjected to image preprocessing, edge detection, axle number identification, axle-to-axle ratio calculation, weight limit determination, and the like. Specifically, the image preprocessing may include image graying, Gamma image correction, and the like. Edge detection may include Sobel edge operator detection. Identifying the number of axes may include processes such as Hough algorithm transformation detection circle and CURE merging clustering algorithm. The method for identifying the weight limit of the truck on the expressway based on the image processing is mainly based on the development of artificial intelligence, and is used for identifying the axle of the truck based on the basis of the image identification to determine the type of the truck and the weight limit corresponding to the type of the truck, so that the expressway toll station is helped to judge whether the truck is overweight, and the use of manpower physics and the like is reduced.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
It should be noted that the terms "first \ second \ third" referred to in the embodiments of the present application merely distinguish similar objects, and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may exchange a specific order or sequence when allowed. It should be understood that "first \ second \ third" distinct objects may be interchanged under appropriate circumstances such that the embodiments of the application described herein may be implemented in an order other than those illustrated or described herein.
The terms "comprising" and "having" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, product, or device.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (5)
1. A method for identifying weight limits of express highway trucks based on image processing is characterized by comprising the following steps:
s10, acquiring a photo of a target truck running on the expressway, and preprocessing the photo of the truck to obtain a corrected photo;
s20, carrying out edge detection on the corrected picture, and determining edge points on the corrected picture;
s30, identifying the number of target axles of the target truck according to the edge points on the corrected image;
s40, calculating the wheelbase ratio among the axles of the target truck;
and S50, determining the weight limit of the target truck according to the target axle number and the axle-to-axle ratio.
2. The image processing-based highway truck weight limit identification method according to claim 1, wherein the preprocessing the truck photo comprises:
graying the wagon photos by adopting a weighted average method principle, setting weights corresponding to various color components, carrying out weighted average on the grayed wagon photos, and correcting pixels which are not in a preset grayscale range in the wagon photos by adopting gamma verification.
3. The image processing-based highway truck weight limit identification method according to claim 1, wherein the edge detection of the corrected photo comprises:
and adopting a Sobel edge detection operator to carry out edge detection on the corrected photo.
4. The image processing-based highway truck weight limit identification method according to claim 1, wherein the identifying the number of target axles of the target truck according to the edge points on the corrected image comprises:
identifying edge points in the circular images in the corrected images, mapping the edge points in the circular images into a parameter space from an image space, searching the circle centers of all the circular images in the parameter space, and solving the radius coordinates of all the circular images so as to finally determine all initial circles in the corrected images;
determining a selection cluster according to each initial circle, taking a point which is farthest from the centroid of the corresponding selection cluster in each selection cluster as a first selection point, then sequentially selecting the point which is farthest from the selected selection point as the selection point until more than 10 selection points are selected, and determining a circle which takes the centroid as the center of a circle and passes through the selection point to draw out a plurality of selection circles;
contracting the selection circle to the corresponding selection cluster according to the preset circle parameter to obtain a contracted circle, determining the circle center of the contracted circle as a representative point, determining initial clustering data according to the contracted circle within the preset circle parameter range, circulating a CURE algorithm, clustering by adopting the mass center of each selection cluster and the distance of the representative point to obtain an axle circle representing an axle, and determining the target axle number of the target truck according to each axle circle.
5. The image processing-based express way truck weight limit identification method according to claim 1, wherein the determining the weight limit of the target truck according to the target axle number and the axle-to-axle distance ratio comprises:
obtaining an axis parameter-weight limit comparison table; the axle parameter-weight limit comparison table records the corresponding relation between the number of axles of the truck and the weight limit;
searching the weight limit corresponding to the target axis number in the axis parameter-weight limit comparison table;
and if a plurality of weight limits corresponding to the target axle number are obtained, determining the axle base ratio to the corresponding weight limit as the weight limit of the target truck.
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