CN112288717A - Method for detecting foreign matters on side part of motor train unit train - Google Patents
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Abstract
A method for detecting foreign matters on the side part of a motor train unit train relates to the technical field of image processing, and aims to solve the problems of low detection efficiency and accuracy when the foreign matters exist in skirt boards on the side part of the motor train unit train through manual detection in the prior art, and comprises the following steps: acquiring a two-dimensional image of the train; step two: intercepting an area image of a skirt board position on the side part of the train: step three: carrying out gray level normalization processing on the area image; step four: performing morphological processing on the image after the gray level normalization, and performing background modeling according to the fixed characteristics of the apron board; step five: carrying out differential matching on different skirt board images and background models corresponding to the skirt board images; step six: and performing morphological processing on the image after the difference matching: step seven: an area threshold value, a length threshold value, a width threshold value and an aspect ratio threshold value of the contour are set, and the morphologically processed image is compared with the threshold values to determine whether or not foreign matter is present.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image processing method for detecting side foreign matters based on characteristic gray scale normalization of side parts of a motor train unit train.
Background
The skirt board at the side part of the motor train unit is easy to adhere to foreign matters such as bottles and plastic bags in the running process of the motor train unit, if the foreign matters are not found and cleaned in time, the driving safety is endangered, fault detection is carried out in a mode of manually checking images, the motor train unit runs at a high density, the time for detecting the motor train unit is short, the conditions of fatigue, omission and the like are easily caused in the working process of a motor train unit detector, the detection omission and the false detection are caused, the driving safety is influenced, manual detection is adopted, and the detection efficiency and the accuracy are low.
Disclosure of Invention
The purpose of the invention is: the method for detecting the foreign matters on the side part of the motor train unit train is provided aiming at the problems of low detection efficiency and accuracy when the foreign matters exist in the skirt board on the side part of the motor train unit train through manual detection in the prior art.
The technical scheme adopted by the invention to solve the technical problems is as follows:
a method for detecting foreign matters on the side of a motor train unit train comprises the following steps:
the method comprises the following steps: acquiring a two-dimensional image of the train;
step two: intercepting an image of a skirt board area at the side part of the train:
step three: carrying out gray level normalization processing on the images of the skirt board area at the side part of the train;
step four: carrying out background modeling according to the fixed characteristics of the images of the skirt board area at the side part of the train;
step five: carrying out difference matching on the normalized train side apron board area image and a background model corresponding to the train side apron board area image;
step six: and performing morphological processing on the image after the difference matching:
step seven: and setting an area threshold value, and length, width threshold values and aspect ratio threshold values of the contour, comparing the morphologically processed image with each threshold value, and if the morphologically processed image is matched with each threshold value, determining that no foreign matter exists, otherwise, determining that the foreign matter exists.
Further, the third step comprises the following specific steps:
fixed characteristic and row according to images of skirt board area at side part of trainObtaining an image high pixel value ratio threshold R by a two-dimensional image pixel value of a vehicleH0And image low pixel value ratio threshold RL0Then, the pixel values are divided into 255 levels, the size of the currently selected side skirt board image is W multiplied by H, wherein W is the width of the image, H is the height of the image, the value of the pixel value of a certain point in the image after gray level normalization is Y, and the threshold value of the high pixel value is YH0＝W×H×RH0The threshold value of the low pixel value is YL0＝W×H×RL0The value of Y is obtained by the following formula:
further, the background modeling in the fourth step comprises the following steps: firstly, dividing an image of a skirt board area at the side part of a train into an upper part, a middle part and a lower part, and modeling a background image according to the image characteristics of three parts on different carriages.
Further, the background modeling in the fourth step specifically comprises the following steps:
according to the position of the grating in the image of the skirt board area at the side part of the train, the position above the grating is regarded as the upper part, the position where the grating is located is regarded as the middle part, the part below the grating is regarded as the lower part, the image after the gray normalization is divided into three parts of image areas corresponding to the upper part, the middle part and the lower part respectively according to the proportion in the height direction, and background difference matching is carried out respectively.
Further, the morphological processing in the fourth step comprises binarization, filtering, expansion and corrosion.
Further, the method also comprises a step of positioning the foreign body, and the specific steps are as follows:
and determining the position of the foreign matter in the position area image of the skirt board at the side part of the train according to the position of the foreign matter, and mapping the position of the foreign matter on the whole image of the train through position mapping to position the foreign matter.
The invention has the beneficial effects that:
1. and the automatic image identification mode is used for replacing manual detection, so that the detection efficiency and accuracy are improved.
2. Before fault detection, the train image is processed by using an improved gray level normalization algorithm, so that the gray level balance of the image is ensured, and a guarantee is provided for the fault detection of the subsequent image processing.
3. The invention improves the traditional gray normalization processing, and is more suitable for detecting the vehicle images running in different environments.
4. The method adopts a background modeling mode to model the characteristics of the skirt board at the side part of the train, and compared with the traditional interframe difference method, the method utilizes more characteristics of the skirt board at the side part of the train, and further improves the accuracy and efficiency of foreign matter detection.
Drawings
FIG. 1 is a flow chart of the fault identification of the present invention;
FIG. 2 is a flow chart of background modeling in accordance with the present invention.
Detailed Description
The first embodiment is as follows: specifically describing the embodiment with reference to fig. 1 and 2, the method for detecting foreign matter on the side of the motor train unit train in the embodiment comprises the following steps:
the method comprises the following steps: acquiring a two-dimensional image of the train;
step two: intercepting an image of a skirt board area at the side part of the train:
step three: carrying out gray level normalization processing on the images of the skirt board area at the side part of the train;
step four: carrying out background modeling according to the fixed characteristics of the images of the skirt board area at the side part of the train;
step five: carrying out difference matching on the normalized train side apron board area image and a background model corresponding to the train side apron board area image;
step six: and performing morphological processing on the image after the difference matching:
step seven: and setting an area threshold value, and length, width threshold values and aspect ratio threshold values of the contour, comparing the morphologically processed image with each threshold value, and if the morphologically processed image is matched with each threshold value, determining that no foreign matter exists, otherwise, determining that the foreign matter exists.
Linear array image acquisition
High-definition equipment is set up around the train track respectively, and the train acquires high-definition images after passing through the equipment. By adopting the line scanning mode, a two-dimensional image with wide visual field and high precision can be formed.
Intercepting area to be detected
The two-dimensional images are spliced and intercepted according to the key parts of the train according to the wheel base information and the train type information of the train, and the images of the apron board positions are obtained from the images of the whole train, so that the time occupied by image processing of the non-side apron boards can be reduced, and the identification accuracy is improved.
Implementation and improvement of algorithms
The whole image processing process is divided into an image preprocessing process and an image processing process of foreign matter detection.
In the preprocessing process, the invention selects an improved gray normalization method, each section of train has the fixed characteristic mainly according to the characteristic of high similarity of the train of the bullet train, the area ratio of the skirting board grating in the current section of train exceeds sixty percent, the pixel value of the skirting board grating in the normal image is relatively fixed in the whole section of train, and the pixel threshold value is selected according to the characteristic of the skirting board grating to perform gray normalization on the image at the side part of the train. The method enhances the quality of the image based on the characteristic gray scale normalization of the parts at the sides of the motor train unit train. The running environment of the train is complex, the image is influenced by external factors such as weather, the quality of the image can be improved through improved image gray level normalization processing, the gray level balance of the side skirt board image for foreign matter detection is guaranteed, and the generalization performance of the foreign matter detection is higher.
The method comprises the steps of carrying out background modeling according to the fixed characteristics of a side skirt board, dividing the side skirt board part of a train into an upper part, a middle part and a lower part, modeling a background image according to the characteristics of images of the three parts of different trains, and carrying out morphological processing such as binaryzation, swelling corrosion and the like on the modeled background image. And then, according to different bus sections, the image of the skirt board at the side part of the current passing bus section is subjected to differential matching with the corresponding background template, the differential image is subjected to morphological image processing such as expansion and corrosion, and the final binary image is subjected to fault judgment.
The historical passing image with better image quality is selected, the side skirt board regards the position above the grating as the upper part, the position of the grating as the middle part and the part below the grating as the lower part according to the position of the grating. When the side foreign matter detection is performed, the image with the normalized gray scale is divided into the three image areas of the upper part, the middle part and the lower part according to the proportion in the height direction, and the background difference is performed on the image areas. Different areas adopt different threshold values to carry out differential calculation, and the foreign body fault of the vehicle can be accurately detected
The second embodiment is as follows: this embodiment is a further improvement of the first embodiment, and is different from the first embodiment in that the third step includes:
obtaining an image high pixel value ratio threshold value R according to the fixed characteristics of the image of the lateral skirt board area of the train and the pixel value of the two-dimensional image of the trainH0And image low pixel value ratio threshold RL0Then, the pixel values are divided into 255 levels, the size of the currently selected side skirt board image is W multiplied by H, wherein W is the width of the image, H is the height of the image, the value of the pixel value of a certain point in the image after gray level normalization is Y, and the threshold value of the high pixel value is YH0＝W×H×RH0The threshold value of the low pixel value is YL0＝W×H×RL0The value of Y is obtained by the following formula:
definition of image grayscale normalization
The gray normalization is a common image preprocessing process, and because the train images acquired by the images at different time and under different illumination may have great difference in gray distribution, the gray distribution of the images is not concentrated, which directly affects subsequent feature extraction and identification, the gray normalization of the train images is required. According to the method, the improved gray level normalization processing is adopted according to the characteristics of the skirt board image at the side part of the train, compared with the traditional mean variance normalization and gray level change normalization, the gray level of the processed image is more convenient for subsequent processing, and the generalization capability of the whole identification process is improved.
Selecting an image high pixel value ratio threshold value R according to the image characteristics of the skirt board at the side of the motor train unit trainH0The low pixel value of the image is the ratio threshold of RL0。RH0，RL0And the image pixel value is obtained by conjecture according to the current station passing vehicle image pixel value. The pixel values are divided into 255 levels. Suppose the currently selected side skirt image size is W × H, where H is the image width and H is the image height.
The value of a pixel value of a certain point in the image after gray level normalization is Y, and the value of Y is obtained through the following formula:
wherein the threshold value of the high pixel value is YH0＝W×H×RH0The threshold value of the low pixel value is YL0＝W×H×RL0
The third concrete implementation mode: the present embodiment is a further improvement of the second embodiment, and the difference between the present embodiment and the second embodiment is that the background modeling step in the fourth step is: firstly, dividing an image of a skirt board area at the side part of a train into an upper part, a middle part and a lower part, and modeling a background image according to the image characteristics of three parts on different carriages.
Background modeling
The method has the advantages that the side images are divided into an upper area, a middle area and a lower area according to inherent part characteristics of the skirt board images at the side of the motor train unit, background modeling is carried out on the images of different motor trains, so that influences of various cover plates, bolts and grids can be effectively avoided, and accuracy and high efficiency of foreign matter detection are guaranteed. The flow is shown in detail in FIG. 2.
The fourth concrete implementation mode: the present embodiment is a further improvement of the third embodiment, and the difference between the present embodiment and the third embodiment is that the specific steps of background modeling in the fourth step are as follows:
according to the position of the grating in the image of the skirt board area at the side part of the train, the position above the grating is regarded as the upper part, the position where the grating is located is regarded as the middle part, the part below the grating is regarded as the lower part, the image after the gray normalization is divided into three parts of image areas corresponding to the upper part, the middle part and the lower part respectively according to the proportion in the height direction, and background difference matching is carried out respectively.
Differential matching
The method comprises the steps of carrying out differential matching on side skirt board images of different trains and corresponding background models, then carrying out morphological processing on the images, judging whether foreign matters exist or not according to a detected component module in the images and different from other components in the side images of the trains by setting an area threshold, a length threshold, a width threshold and an aspect ratio threshold of a contour, finally judging whether the foreign matters exist or not, determining the position in a cut small graph according to the position of the detected foreign matters, mapping the position in the cut small graph to the whole image of the train through position mapping, generating a message and positioning faults.
The fifth concrete implementation mode: the present embodiment is a further improvement of the third embodiment, and the difference between the third embodiment and the present embodiment is that the morphological processing in the fourth step includes binarization, filtering, swelling and erosion.
Basic morphological treatment
And performing morphological processing such as binarization, filtering, expansion corrosion and the like on the image after gray level equalization to remove the influence of cavities and noise.
The sixth specific implementation mode: the third embodiment is a further improvement of the third embodiment, and the difference between the third embodiment and the fourth embodiment is that the method further comprises a step of positioning the foreign matter, and the specific steps are as follows:
and determining the position of the foreign matter in the position area image of the skirt board at the side part of the train according to the position of the foreign matter, and mapping the position of the foreign matter on the whole image of the train through position mapping to position the foreign matter.
Fault detection
The flow chart is shown in FIG. 1
The method comprises the following steps: intercepting candidate area to be detected
And taking out an image of the lateral skirt board area to be detected according to the train type information and the prior knowledge of the area where the lateral skirt board is located.
Step two: carrying out gray level normalization processing on the image
Improved gray scale normalization processing for the resulting target group and image
Step three: offline background modeling
Background template creation for image according to characteristics of side skirt board through vehicle type information
Step four: fault determination
And judging the foreign matter fault through the current image information and the background template information and through morphological image processing modes such as filtering, expansion, corrosion and the like.
Step five: upload alarm
And generating a message according to the obtained fault information of the fault type and the fault position, and uploading the message to an alarm platform.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (6)
1. A method for detecting foreign matters on the side of a motor train unit train is characterized by comprising the following steps:
the method comprises the following steps: acquiring a two-dimensional image of the train;
step two: intercepting an image of a skirt board area at the side part of the train:
step three: carrying out gray level normalization processing on the images of the skirt board area at the side part of the train;
step four: carrying out background modeling according to the fixed characteristics of the images of the skirt board area at the side part of the train;
step five: carrying out difference matching on the normalized train side apron board area image and a background model corresponding to the train side apron board area image;
step six: and performing morphological processing on the image after the difference matching:
step seven: and setting an area threshold value, and length, width threshold values and aspect ratio threshold values of the contour, comparing the morphologically processed image with each threshold value, and if the morphologically processed image is matched with each threshold value, determining that no foreign matter exists, otherwise, determining that the foreign matter exists.
2. The method for detecting the foreign matters on the side of the motor train unit train as claimed in claim 1, wherein the third step comprises the following specific steps:
obtaining an image high pixel value ratio threshold value R according to the fixed characteristics of the image of the lateral skirt board area of the train and the pixel value of the two-dimensional image of the trainH0And image low pixel value ratio threshold RL0Then, the pixel values are divided into 255 levels, the size of the currently selected side skirt board image is W multiplied by H, wherein W is the width of the image, H is the height of the image, the value of the pixel value of a certain point in the image after gray level normalization is Y, and the threshold value of the high pixel value is YH0＝W×H×RH0The threshold value of the low pixel value is YL0＝W×H×RL0The value of Y is obtained by the following formula:
3. the method for detecting the foreign matters on the side of the motor train unit train as claimed in claim 2, wherein the background modeling in the fourth step comprises the following steps: firstly, dividing an image of a skirt board area at the side part of a train into an upper part, a middle part and a lower part, and modeling a background image according to the image characteristics of three parts on different carriages.
4. The method for detecting the foreign matters on the side of the motor train unit train as claimed in claim 3, wherein the background modeling in the fourth step comprises the following specific steps:
according to the position of the grating in the image of the skirt board area at the side part of the train, the position above the grating is regarded as the upper part, the position where the grating is located is regarded as the middle part, the part below the grating is regarded as the lower part, the image after the gray normalization is divided into three parts of image areas corresponding to the upper part, the middle part and the lower part respectively according to the proportion in the height direction, and background difference matching is carried out respectively.
5. The method for detecting the foreign matters on the side of the motor train unit train as claimed in claim 3, wherein the morphological processing in the fourth step includes binarization, filtering, expansion and corrosion.
6. The method for detecting the foreign matters on the side of the motor train unit train as claimed in claim 3, wherein the method further comprises the step of positioning the foreign matters, and the specific steps are as follows:
and determining the position of the foreign matter in the position area image of the skirt board at the side part of the train according to the position of the foreign matter, and mapping the position of the foreign matter on the whole image of the train through position mapping to position the foreign matter.
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