CN111539421A - Deep learning-based railway locomotive number identification method - Google Patents
Deep learning-based railway locomotive number identification method Download PDFInfo
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Abstract
A railway locomotive number identification method based on deep learning relates to the technical field of railway vehicle fault identification. The invention aims to solve the problems that in the process of manually identifying the locomotive number, the detection result is low in precision and efficiency, and the timely searching of the fault reason is not facilitated. According to the method for identifying the railway locomotive number based on deep learning, the small characters in the locomotive number can be effectively identified by adopting a mode of firstly carrying out rough positioning on the target detection network and then carrying out identification in the positioned screenshot, so that the identification accuracy is improved, and missing detection and wrong detection are avoided. Due to the fact that the types of the colors of the vehicles are too many, the contrast of the car numbers of the colors shot by the black-and-white camera is not high. The invention adopts the color area-array cameras on the two sides of the vehicle body to shoot the vehicle number image, and can effectively avoid the influence of strong sunlight interference, rain, snow and other severe weather on the single-side camera.
Description
Technical Field
The invention belongs to the technical field of railway vehicle fault identification.
Background
The train number of a railway locomotive is an important identification feature of the locomotive, is accompanied with the complete operation process of a truck, and plays a very critical role in the operation process of the railway.
At present, the fault detection of the railway freight car generally adopts a manual troubleshooting mode. The investigation process is greatly influenced by factors such as the business quality, the responsibility and the labor intensity of operators, so that the conditions of missing inspection, operation simplification and the like are easy to occur.
Disclosure of Invention
The invention provides a railway locomotive number identification method based on deep learning, aiming at solving the problems that in the process of manually identifying the railway locomotive number, the detection result is low in precision and efficiency, and the failure reason is not easy to find in time.
The method for identifying the locomotive number based on deep learning comprises the following steps:
the method comprises the following steps: a training set is established, and the training set is established,
collecting car number pictures of different types of vehicles at two sides of the vehicle at different time and under different environments, establishing a sample library by utilizing all the car number pictures,
respectively marking the position of the car number in each car number picture, generating corresponding label files, taking all the car number pictures and the corresponding label files as a training set of the preposed target detection network,
intercepting the car number picture according to the position of the car number in each car number picture to obtain a screenshot containing the car number, marking characters of the car number in the screenshot, marking different characters as different types, and taking all the screenshots and the types corresponding to the characters in the screenshots as a training set of a car number recognition network;
step two: the training of the weight is carried out,
training the target detection network using data in the training set of the pre-target detection network,
training a car number recognition deep learning network by using data in a training set of the car number recognition network;
step three: the collection of the image is carried out,
collecting car number images on two sides of a vehicle to be detected, and adjusting the two car number images into a picture to be detected with pixels of 512 multiplied by 512;
step four: the identification of the number of the car,
inputting two pictures to be detected into a target detection network to obtain a position frame of the car number in the pictures to be detected,
intercepting the detection picture according to the position frame of the car number in the picture to be detected to obtain two car number screenshots, respectively inputting the two car number screenshots into a car number recognition deep learning network to obtain car number types corresponding to more car number screenshots,
step five: as a result of the determination,
judging whether the confidence degrees of the two car number types are the same,
if so, the two screenshots of the car numbers have the same car number type and can be used as the output result of the car numbers,
otherwise, the type of the car number with high confidence coefficient is used as a car number output result.
After the car number images at the two sides of the vehicle to be detected are collected, firstly, the two car number images are respectively subjected to projection transformation to correct the image distortion,
then, the color, brightness and contrast of the two car number images are respectively adjusted, the color of the images is corrected,
and finally, adjusting image pixels to obtain the picture to be detected.
And when the target detection network outputs the position frames where the plurality of car numbers are located, taking the position frame with the maximum reliability as a final result.
In practical application, because some characters in the car number are too small in the image, the accuracy of the small target recognition detection in the deep learning field is not high all the time. Therefore, the method for identifying the railway locomotive number based on deep learning, provided by the invention, has the advantages that the small characters in the locomotive number can be effectively identified by adopting a mode of firstly carrying out rough positioning on the target detection network and then carrying out identification in the positioned screenshot, the identification accuracy is improved, and missing detection and wrong detection are avoided.
Due to the fact that the types of the colors of the vehicles are too many, the contrast of the car numbers of the colors shot by the black-and-white camera is not high. The invention adopts the color area-array cameras on the two sides of the vehicle body to shoot the vehicle number image, and can effectively avoid the influence of strong sunlight interference, rain, snow and other severe weather on the single-side camera.
Drawings
FIG. 1 is a network training flow diagram;
fig. 2 is a flow chart of car number identification.
Detailed Description
As the types of the railway locomotives are various, the writing fonts, the sizes, the colors, the positions and the like of the locomotive numbers of different types are different. The conventional image processing method has difficulty in recognizing all kinds of car numbers.
With the great improvement of the processing performance of chip hardware, a foundation is provided for the complex computation of a deep network. The deep learning is widely applied to the field of image processing, and compared with the traditional mode, the deep learning integrates the feature learning into the process of establishing the model, so that the accuracy and efficiency of fault detection can be effectively improved.
Therefore, the invention provides the following embodiment, and the car number is identified by adopting a deep learning mode.
The first embodiment is as follows: specifically describing the present embodiment with reference to fig. 1 and 2, the method for identifying a railroad locomotive number based on deep learning according to the present embodiment includes the steps of:
the method comprises the following steps: a training set is established, and the training set is established,
the high-definition image acquisition equipment arranged on the two sides of the track is used for acquiring a large number of car number pictures of vehicles with different types on the two sides of the vehicles at different time and in different environments, and the car number pictures are used as initial data sets. In order to enrich the types and the quantity of samples, the pictures in the initial data set are respectively subjected to transformation such as stretching and rotation, so that the initial data set is amplified, and the robustness and the adaptability of the training result are improved. And finally, establishing a sample library by using all car number pictures.
And respectively marking the position of the car number in each car number picture, generating corresponding label files, and taking all the car number pictures and the corresponding label files as a training set of the preposed target detection network.
And intercepting the car number picture according to the position of the car number in each car number picture to obtain a screenshot containing the car number, marking the characters of the car number in the screenshot, marking different characters as different types, and taking all the screenshots and the types corresponding to the characters in the screenshots as a training set of a car number recognition network.
Step two: the training of the weight is carried out,
the target detection network for positioning the train number position adopts an SSD deep learning network, wherein an inclusion v2 is adopted as a feature extraction network of the network, and the target detection network is trained by utilizing data in a training set of a front target detection network. In the embodiment, in order to save time and improve efficiency, the SSD deep learning network with the highest speed is adopted, and the inclusion v2 feature extraction network is adopted to effectively improve efficiency and save time.
Training a car number recognition deep learning network by using data in a training set of the car number recognition network; the vehicle number recognition deep learning network is a faster rcnn network, wherein VGG-16 is adopted as a feature extraction network of the network. VGG-16 has higher operating efficiency relative to resnet, and has higher identification precision relative to inclusion v 2.
Step three: the collection of the image is carried out,
the method comprises the steps of collecting car number images on two sides of a vehicle to be detected, firstly, respectively carrying out projection transformation on the two car number images, and correcting image distortion caused by camera elevation shooting.
Then, the color, brightness and contrast of the two car number images are respectively adjusted, and image changes caused by different illumination conditions are corrected.
And finally, adjusting the two car number images into to-be-detected pictures with pixels of 512 multiplied by 512.
Step four: the identification of the number of the car,
the learning rate of the target detection network is set to 0.0001 by the callback parameter, and the extended anchor point is set to [0.1,0.2,0.34,0.48,0.62,0.76,0.80,0.94 ].
Because the height positions of the car numbers in the images of different car types are different, the car number images with a large range need to be acquired. But can not directly locate and classify to the smaller characters in the car number picture with a larger range. Therefore, the present embodiment adopts a policy of first locating and then identifying the car number.
And inputting the two pictures to be detected into a target detection network, wherein in the obtained result, a plurality of detection result position frames sometimes appear in the same picture, at the moment, the confidence degrees of different position frames are compared, and the position frame with the maximum confidence degree is taken as a final result, namely the position frame where the car number is located in the pictures to be detected is obtained.
And intercepting the detection picture according to the position frame where the car number is in the picture to be detected to obtain two car number screenshots, and respectively inputting the two car number screenshots into a car number recognition deep learning network to obtain the car number types corresponding to the two car number screenshots.
Step five: as a result of the determination,
because the color area-array camera is adopted to shoot the car number picture, the car number picture is easily interfered by sunlight. When sunlight is strong, the side of the vehicle body is easy to reflect light, so that picture information is not clear. Therefore, in the present embodiment, the car numbers on both sides of the vehicle body are recognized by using a mode in which two cameras simultaneously photograph the car numbers on both sides of the vehicle body. So that the car number types corresponding to the two car number screenshots can be obtained. At this time, whether the confidence degrees of the two car number types are the same or not is judged,
when the types of the car numbers on the two sides are the same, the car numbers can be used as the output result of the car numbers,
and when the car number types on the two sides are different, the car number type with high confidence coefficient is used as a car number output result. At this point, the identification of the car number is completed.
Claims (8)
1. The method for identifying the locomotive number based on deep learning is characterized by comprising the following steps of:
the method comprises the following steps: a training set is established, and the training set is established,
collecting car number pictures of different types of vehicles at two sides of the vehicle at different time and under different environments, establishing a sample library by utilizing all the car number pictures,
respectively marking the position of the car number in each car number picture, generating corresponding label files, taking all the car number pictures and the corresponding label files as a training set of the preposed target detection network,
intercepting the car number picture according to the position of the car number in each car number picture to obtain a screenshot containing the car number, marking characters of the car number in the screenshot, marking different characters as different types, and taking all the screenshots and the types corresponding to the characters in the screenshots as a training set of a car number recognition network;
step two: the training of the weight is carried out,
training the target detection network using data in the training set of the pre-target detection network,
training a car number recognition deep learning network by using data in a training set of the car number recognition network;
step three: the collection of the image is carried out,
collecting car number images on two sides of a vehicle to be detected, and adjusting the two car number images into a picture to be detected with pixels of 512 multiplied by 512;
step four: the identification of the number of the car,
inputting two pictures to be detected into a target detection network to obtain a position frame of the car number in the pictures to be detected,
intercepting the detection picture according to the position frame of the car number in the picture to be detected to obtain two car number screenshots, respectively inputting the two car number screenshots into a car number recognition deep learning network to obtain car number types corresponding to the two car number screenshots,
step five: as a result of the determination,
judging whether the confidence degrees of the two car number types are the same,
if so, the two screenshots of the car numbers have the same car number type and can be used as the output result of the car numbers,
otherwise, the type of the car number with high confidence coefficient is used as a car number output result.
2. The method for identifying a railroad locomotive number based on deep learning of claim 1, wherein the pictures of the number of the two sides of the vehicle are collected by high-definition image collecting devices disposed around the track of the vehicle.
3. The method for identifying a railroad locomotive number based on deep learning of claim 1, wherein the sample library further comprises a stretched, rotated and mirrored picture of the acquired car number picture.
4. The deep learning based recognition method of railroad locomotive number according to claim 1,
after the car number images at the two sides of the vehicle to be detected are collected, firstly, the two car number images are respectively subjected to projection transformation to correct the image distortion,
then, the color, brightness and contrast of the two car number images are respectively adjusted, the color of the images is corrected,
and finally, adjusting image pixels to obtain the picture to be detected.
5. The deep learning-based railroad locomotive number identification method according to claim 1, wherein the learning rate of the target detection network is 0.0001 and the anchor points are [0.1,0.2,0.34,0.48,0.62,0.76,0.80,0.94 ].
6. The deep learning-based railroad locomotive number identification method according to claim 1 or 4,
and when the target detection network outputs the position frames where the plurality of car numbers are located, taking the position frame with the maximum reliability as a final result.
7. The deep learning based railway locomotive number identification method according to claim 1, wherein the target detection network is an SSD deep learning network, and the Incep v2 is adopted as a feature extraction network of the network.
8. The deep learning-based railroad locomotive number identification method according to claim 1, wherein the train number identification deep learning network is a faster rcnn network, wherein VGG-16 is used as a feature extraction network of the network.
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| CN113920140A (en) * | 2021-11-12 | 2022-01-11 | 哈尔滨市科佳通用机电股份有限公司 | Wagon pipe cover falling fault identification method based on deep learning |
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