CN115527018A - Fault identification method and device for parts formed by lower lock pins of railway wagon - Google Patents

Fault identification method and device for parts formed by lower lock pins of railway wagon Download PDF

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CN115527018A
CN115527018A CN202211313616.5A CN202211313616A CN115527018A CN 115527018 A CN115527018 A CN 115527018A CN 202211313616 A CN202211313616 A CN 202211313616A CN 115527018 A CN115527018 A CN 115527018A
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image
fault
feature
lock pin
lower lock
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邓艳
马凌宇
秦昌
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Harbin Kejia General Mechanical and Electrical Co Ltd
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Abstract

A fault identification method and device for accessories formed by a lower lock pin of a railway wagon relate to the technical field of railway wagon detection. The invention aims to solve the problems of false detection and high omission factor of the traditional method for manually detecting whether the accessories formed by the lower lock pins of the vehicle are lost. According to the invention, high-definition imaging equipment is respectively built around the rail of the truck, and the truck acquires a high-definition image after passing through the equipment. The method comprises the steps of firstly classifying and positioning the car coupler by adopting a deep learning network to obtain position information of the car coupler, and intercepting a lower lockpin to form a subgraph of an accessory according to position prior knowledge of the accessory part formed by the lower lockpin on different car couplers. And detecting the loss fault of the lower lock pin assembly component by using a deep learning network. And uploading and alarming the coupler with the loss of the accessory formed by the lower lock pin, and performing corresponding processing by the staff according to the recognition result to ensure the safe operation of the train.

Description

Fault identification method and device for parts formed by lower lock pins of railway wagon
Technical Field
The invention belongs to the technical field of railway wagon detection, and particularly relates to detection of a fitting formed by a lower lock pin of a railway wagon.
Background
The railway freight car lower lock pin is formed into an accessory and plays an important role in firmly connecting a front car and a rear car by a car coupler. When a truck runs, the railway transportation frequency is high, the abrasion of parts is large, the condition that the lower lockpin assembly is lost is caused, and then the separation accident of the car coupler is caused, so that great potential safety hazards are brought to the running safety. Therefore, it is important for driving safety to check whether the lower lock pin assembly is complete.
The traditional manual detection mode causes the appearance of missed detection and false detection and influences the driving safety because the conditions such as fatigue and omission easily occur to vehicle detection personnel in the working process.
Disclosure of Invention
The invention provides a fault identification method and device for a railway wagon lower lock pin assembly part, aiming at solving the problems of false detection and high missing rate of the traditional method for manually detecting whether the vehicle lower lock pin assembly part is lost.
A fault identification method for a railway wagon lower lock pin assembly accessory specifically comprises the following steps:
acquiring an image of an accessory part formed by a lock pin assembly under a truck to be detected as a detected image, and inputting the detected image into a YOLOv5-SPD network to obtain an identification result;
the YOLOv5-SPD network adopts a non-step convolution network to extract a characteristic subgraph and adopts a method of weighting and fusing target frames to identify faults in the characteristic subgraph.
Further, a feature map is selected from the detected image by adopting a dynamic anchor frame, and the feature map is input into the Yolov5-SPD network.
Further, the dynamic anchor frame comprises an anchor improvement module, a bounded feature fusion module and a target detection module,
the anchor improving module is used for generating candidate frames and screening out the anchor frames which accord with the characteristic size from the candidate frames;
the bounded feature fusion module is used for fusing the edge feature of the anchor frame with the global feature to obtain a fusion feature;
and the target detection module is used for selecting a feature map in the detected image according to the fusion features.
Further, the fusion process of the bounded feature fusion module is as follows:
Figure BDA0003908092270000011
Figure BDA0003908092270000012
wherein, f is a group of compounds represented by,
Figure BDA0003908092270000013
representing a feature extraction function, M edge For edge features, M (i ) In order to be a global feature,
Figure BDA0003908092270000014
it is indicated that the fusion is performed,
Figure BDA0003908092270000021
represents the fused features after fusion of i features, i being the number of features fused, i =1, 2.
Furthermore, high-definition image acquisition equipment is built beside the rail of the truck to be measured, and the high-definition image acquisition equipment is used for acquiring high-definition images of the truck to be measured.
Further, the high-definition image is a grayscale image.
Further, when a fault exists in the detected image, obtaining the coordinate of the fault position and judging whether the coordinate belongs to a preset range, if so, sending out a fault alarm, otherwise, not sending out the alarm.
The utility model provides a railway freight car lower lockpin makes up accessory trouble recognition device, includes specifically that the following unit:
an acquisition unit: used for acquiring an image of the accessory part formed by the lock pin assembly under the tested truck as a tested image,
an identification unit: the system is used for inputting a detected image into a YOLOv5-SPD network to obtain an identification result;
the YOLOv5-SPD network adopts a non-step convolution network to extract a characteristic subgraph and adopts a method of weighting and fusing target frames to identify faults in the characteristic subgraph.
A computer-readable storage medium stores a computer program that, when executed, implements a method of identifying a fitting fault such as a rail wagon lower latch assembly.
Electronic equipment comprises a storage medium, a processor and a computer program which is stored in the storage medium and can run on the processor, wherein the processor executes the computer program to realize the locking pin assembly of the rail wagon, namely a rail wagon, and the accessory fault identification method. The invention has the following beneficial effects:
1. and the automatic image identification mode is used for replacing manual detection, so that the detection efficiency and accuracy are improved.
2. According to the TFDS, the coupler type and the coupler position provided by the frame are automatically identified, the position of a lower lock pin forming part is accurately positioned, and an accurate sub-picture range is provided for detecting a rear fault.
3. Histogram equalization is selected to enhance the image, and the generalization capability of the training model is improved.
4. The improved YOLOv5 network is used for composing the lost fault of the accessory by the lower lock pin, the improved YOLOv5 uses the dynamic anchor frame as input in the input stage, the larger formula in the regression training process of the bounding box is reduced, and the difficulty of model optimization is reduced.
5. The modified YOLOv5 network is utilized to group lock-down pins into an assembly loss fault, and the modified YOLOv5 uses a target box weighted fusion method as a method of a final predicted bounding box. The bounding box with smaller score but containing certain characteristic information is reserved, so that all the characteristic information is ensured, and the condition that the bounding box with high score is directly selected and part of the information is lost is avoided.
6. Aiming at an improved YOLOv5 network, the SPD Conv is used for replacing a traditional CNN and a pooling layer in a backbone network, no information is skipped for complex down-lock pin accessory images, fine-grained information and characteristics can be captured well, and the accuracy of fault detection is improved. The lower lock pin with a complex structure forms an accessory image, and the lower lock pin has higher robustness.
Drawings
FIG. 1 is a flow diagram of dynamic anchor block generation;
FIG. 2 is a schematic diagram of SPD-Conv when the variation factor is taken to be 2;
fig. 3 is a flow chart of fault identification.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The detection efficiency and stability can be improved by adopting an automatic image identification mode. In recent years, deep learning and artificial intelligence are continuously developed, and the technology is continuously mature. Therefore, the method adopts deep learning to perform the accessory loss fault identification by locking pin descending, and can effectively improve the detection accuracy. The method comprises the following specific steps:
the first embodiment is as follows: the method for identifying the fault of the accessory formed by the lower lock pin of the rail wagon in the embodiment specifically comprises the following steps:
acquiring an image of an accessory part formed by a lock pin under a truck to be detected as a detected image, and inputting the detected image into a YOLOv5-SPD network to obtain an identification result;
the YOLOv5-SPD network adopts a non-step convolution network to extract a characteristic subgraph and adopts a method of weighting and fusing target frames to identify faults in the characteristic subgraph.
The lower lock pin is complex in component part structure, multiple in component parts and compact in structure. The method comprises the steps that a lower lockpin is assembled to detect the loss fault of an accessory, the traditional YOLOv5 mainly utilizes CNN and a pooling layer to extract features, fine-grained information is easy to ignore, and an improved YOLOv5-SPD network is formed by combining an SPD Conv network and the YOLOv5 aiming at low resolution and small objects to train a training sample.
The YOLOv5-SPD uses non-stride convolution to replace the traditional CNN and pooling layer, and aiming at complex lower lock pin accessory images, no information is skipped, fine-grained information and characteristics can be captured well, and the accuracy of fault detection is improved. Where the SPD layer downsamples the feature map but retains all information in the channel dimensions so no information is lost.
The original fused feature map is S cp Size is S 1 ×S 2 ×C 1 In which S is 1 Is the height, S, of the image 2 Is the width of the image, C 1 Is the number of channels. The sub-feature map mapping relationship is as follows:
Figure BDA0003908092270000031
Figure BDA0003908092270000041
where scale is the variation factor. Giving a fused feature map S cp Sub feature map f x,y By all S cp (i, j) wherein i + x, i + y are each divisible by a varying factor. Each sub-feature map is passed through S cp And down-sampling the change factor. Fig. 2 is a SPD-Conv schematic diagram of scale = 2. When scale =2, four sub-feature maps f are obtained 0,0 ，f 1,0 ，f 0,1 ，f 1,1 Each sub-feature map has a size of
Figure BDA0003908092270000042
And is provided with
Figure BDA0003908092270000043
Two downsamplings are performed.
These sub-feature maps are then connected along the channel dimension, thereby obtaining a feature map X' having a spatial dimension reduced by scale and a spatial dimension reduced by scale 2 Increased channel dimensions. That is, the SPD will characterize graph X (S, S, C) 1 ) Conversion into intermediate feature maps
Figure BDA0003908092270000044
After SPD, add non-step convolution C 2 A filter, at this time C 2 ＜scale 2 C 1 The characteristic diagram is further changed into
Figure BDA0003908092270000045
The reason we use non-strided convolutions is to preserve as much as possible of all the descriptive feature information. Otherwise, we would "downscale" the profile using a conventional stride =3,3 × 3 filter, but only sample once per pixel. stride =2 asymmetric sampling will occur in the case where even and odd rows or columns are to be sampled at different times.
YOLOv5-SPD replaces the stride convolution with stride 2 in YOLOv5 by SPD-Conv. Where there are 5 substitutions in the Backbone moiety; the portion of the neck (the connection network of the backbone network and the detector head) is replaced by 2, while after each step convolution in YOLOv5 there is a full link layer, and the YOLOv5-SPD continues to maintain a full link layer between SPD (Space-to-depth) and Conv (convolutional layer). The learning of fine-grained information and characteristics is focused, and the accuracy of fault detection is improved.
Improved YOLOv5 is a method of final predicted bounding box using a target box weighted fusion method. The method mainly comprises the steps of sequencing confidence degrees of all prediction frames of a model from top to bottom and placing the confidence degrees into a list, iteratively matching the predicted boundary frames with real boundary frames marked by image targets in a data set, when an IOU (interaction over Unit, a standard for measuring the accuracy of detecting corresponding objects in a specific data set) is larger than a set threshold value of 0.6, considering that matching is successful, placing the predicted boundary frames which are not successfully matched into a new iteration process to continue matching judgment processing, and if matching is successful, calculating coordinates and confidence degrees of the boundary frames. And fusing the predicted boundary frame with all the real boundary frames successfully matched with the predicted boundary frame to obtain the average confidence coefficient of the predicted boundary frame, wherein the coordinate is the weighted sum of the coordinates of all the boundary frames forming the predicted boundary frame, and the weight is the confidence coefficient proportion of the corresponding boundary frame. The bounding boxes that are incorrectly predicted by the model are corrected. The regression frame of the improved model trained by the YOLOv5 network is more appropriate to the real situation, and the accuracy of fault detection can be improved.
In the embodiment, in the improved YOLOv5-SPD network, a dynamic anchor frame is selected as an input, the backbone network uses a non-stride convolutional network to replace a traditional CNN layer and a traditional pooling layer for extracting features, and a method of weighting and fusing target frames is selected to replace a traditional non-maximum suppression (NMS) method for final prediction.
The second embodiment is as follows: the sizes of subgraphs obtained by different coupler types, different stations and different cameras are not unique, the sizes of lower lock pin rented accessories are also changed, the traditional generation mode of the YOLOv5 anchor frame easily causes that small targets are divided into the anchor frames which do not accord with the sizes of the targets, so that great loss is caused in the training process, and meanwhile, the anchor frames are used as input in the model training stage, so that the setting error is large, and the speed and the difficulty of model convergence are increased. In the embodiment, in order to improve the accuracy and speed of model training, a dynamic anchor frame is adopted to select a feature map from a detected image, and the feature map is input into a YOLOv5-SPD network.
The third concrete implementation mode: in this embodiment, the method for identifying a fault of a railway wagon lower lockpin assembly is further described, in which the dynamic anchor frame includes an anchor improvement module, a bounded feature fusion module and a target detection module,
the anchor improving module is used for generating candidate frames and screening the anchor frames which accord with the characteristic size from the candidate frames;
the bounded feature fusion module is used for fusing the edge feature of the anchor frame with the global feature to obtain a fusion feature;
and the target detection module is used for selecting a feature map in the detected image according to the fusion features.
As shown in fig. 1, the ARM generates a candidate frame, performs preliminary screening and fine tuning on the anchor frame, and then transmits the adjusted anchor frame to the ODM module through the connection of the BFF module. BFF can better fuse edge information with region information.
The fourth concrete implementation mode: the embodiment further describes a method for identifying a fault of a railway wagon lower lock pin assembly part, which is described in the third specific embodiment, and in the embodiment, the fusion process of the bounded feature fusion module is as follows:
Figure BDA0003908092270000061
Figure BDA0003908092270000062
wherein, f is a group of compounds represented by,
Figure BDA0003908092270000063
representing a feature extraction function, M edge For edge features, M (i) In order to be a global feature,
Figure BDA0003908092270000064
it is indicated that the fusion is performed,
Figure BDA0003908092270000065
representing i characteristicsFused features after fusion, i being the number of features fused, i =1, 2.
The fifth concrete implementation mode: in the embodiment, a high-definition image acquisition device is built at the track side of the truck to be tested, and the high-definition image acquisition device is used for acquiring a high-definition image of the truck to be tested.
The sixth specific implementation mode is as follows: the present embodiment further describes a method for identifying a fault of a railway wagon lower locking pin assembly, where the method is described in the first embodiment, and in the present embodiment, the high-definition image is a grayscale image.
The seventh embodiment: in the embodiment, when a fault is identified in the detected image, the coordinate of the fault position is obtained and whether the coordinate belongs to a preset range is judged, if so, a fault alarm is given, and otherwise, no alarm is given.
In the method for identifying the fault of the lower lock pin assembly of the railway wagon according to the first to seventh embodiments, the YOLOv5-SPD network needs to be trained first. Specifically, high-definition equipment is firstly built around the rail of the truck respectively, and the truck acquires a high-definition image after passing through the equipment. The image is a sharp grayscale image. The truck parts can be influenced by rain, mud, oil, black paint and other natural conditions or artificial conditions. Also, there may be differences in the images taken at different sites. Therefore, the lower lock pin is very different from the accessory image. Therefore, in the process of collecting the image data of the lower lock pin assembly, the images of the lower lock pin assembly under various conditions are collected as completely as possible to ensure diversity.
Comprehensively judging the coupler type and the coupler position provided by a TFDS (vehicle safety monitoring system) system frame pilot module to obtain the coordinate position of the accessory formed by the lower lock pins, intercepting the sub-image of the accessory formed by the lower lock pins according to the obtained coordinate position, and finally obtaining a training sample image.
Although the creation of the sample data set includes images under various conditions, data amplification of the sample data set is still required to improve the stability of the algorithm. The histogram equalization image is used for enhancement, so that the diversity and the applicability of the sample can be ensured to the maximum extent. Meanwhile, the histogram equalization can improve the brightness of the image and enhance the characteristic information of the lower lock pin accessory.
And training the YOLOv5-SPD network by using the collected sample data set. After training, inputting the tested truck information into the trained YOLOv5-SPD network, and identifying the fault of the truck lower lockpin assembly, as shown in FIG. 3.
The specific implementation mode is eight: the accessory fault recognition device formed by combining the lower lock pins of the railway wagon in the embodiment specifically comprises the following units:
an acquisition unit: used for acquiring an image of the accessory part formed by the lock pin assembly under the tested truck as a tested image,
an identification unit: the system is used for inputting a detected image into a YOLOv5-SPD network to obtain an identification result;
the YOLOv5-SPD network adopts a non-step convolution network to extract a characteristic subgraph and adopts a method of weighting and fusing target frames to identify faults in the characteristic subgraph.
The specific implementation method nine: a computer-readable storage medium according to the present embodiment stores a computer program, and when the computer program is executed, the method according to any one of the first to seventh embodiments is implemented.
The specific implementation mode is ten: the electronic device according to this embodiment includes a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, and the processor executes the computer program to implement any one of the methods according to the first to seventh embodiments.
According to the invention, high-definition imaging equipment is respectively built around the rail of the truck, and the truck acquires a high-definition image after passing through the equipment. The method comprises the steps of adopting a deep learning network, firstly classifying and positioning the car coupler to obtain position information of the car coupler, and intercepting a lower lockpin to form a subgraph of an accessory according to position prior knowledge of the accessory part formed by the lower lockpin on different car couplers. And detecting the loss fault of the lower lock pin assembly accessory by using a deep learning network. And uploading and alarming the coupler with the lower lock pin assembly and part loss, and carrying out corresponding processing by the staff according to the identification result to ensure the safe operation of the train.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present disclosure has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that various changes, modifications and equivalents can be made in the embodiments of the invention without departing from the scope of the invention as set forth in the claims.

Claims (10)

1. A fault identification method for accessories formed by lower lock pins of a rail wagon comprises the following specific steps:
acquiring an image of an accessory part formed by a lock pin assembly under a truck to be detected as a detected image, and inputting the detected image into a YOLOv5-SPD network to obtain an identification result;
the method is characterized in that the YOLOv5-SPD network adopts a non-step convolution network to extract a characteristic subgraph and adopts a target frame weighting fusion method to identify faults in the characteristic subgraph.
2. The method for identifying the fault of the assembly of the locking pin of the rail wagon according to claim 1, wherein a dynamic anchor frame is adopted to select a feature map in the tested image, and the feature map is input into a Yolov5-SPD network.
3. The method of claim 2, wherein the dynamic anchor block comprises an anchor improvement module, a bounded feature fusion module and a target detection module,
the anchor improving module is used for generating candidate frames and screening out anchor frames which accord with the characteristic size from the candidate frames;
the bounded feature fusion module is used for fusing the edge feature of the anchor frame with the global feature to obtain a fusion feature;
and the target detection module is used for selecting a feature map in the detected image according to the fusion feature.
4. The method for identifying the fault of the assembly of the lower lock pin of the railway wagon as claimed in claim 3, wherein the fusion process of the bounded feature fusion module is as follows:
Figure FDA0003908092260000011
Figure FDA0003908092260000012
wherein, the f is a characteristic of the compound,
Figure FDA0003908092260000013
representative feature extraction function, M edge For edge features, M (i) In order to be a global feature,
Figure FDA0003908092260000014
it is indicated that the fusion is performed,
Figure FDA0003908092260000015
represents the fused features after fusion of i features, i being the number of features fused, i =1, 2.
5. The method for identifying the fault of the fitting formed by the lower lock pin of the railway wagon as claimed in claim 1, wherein a high-definition image acquisition device is built beside a rail of the wagon to be tested, and the high-definition image acquisition device is used for acquiring a high-definition image of the wagon to be tested.
6. The method for identifying the fault of the assembly of the lower lock pin of the railway wagon as claimed in claim 5, wherein the high-definition image is a grayscale image.
7. The method for identifying the fault of the accessory assembled by the lower lockpin of the railway wagon as claimed in claim 1, wherein when the detected image is identified to have the fault, the coordinate of the fault position is obtained and whether the coordinate belongs to a preset range is judged, if so, a fault alarm is given out, and otherwise, the fault alarm is not given out.
8. The utility model provides a railway freight car lower lockpin makes up accessory trouble recognition device, includes specifically that the following unit:
an acquisition unit: used for acquiring an image of the accessory part formed by the lock pin assembly under the tested truck as a tested image,
an identification unit: the image recognition system is used for inputting the tested image into a YOLOv5-SPD network to obtain a recognition result;
the method is characterized in that the YOLOv5-SPD network adopts a non-step convolution network to extract a characteristic subgraph and adopts a target frame weighting fusion method to identify faults in the characteristic subgraph.
9. A storage medium readable by a computer, the storage medium storing a computer program, the computer program when executed implementing the method of any of claims 1 to 7.
10. Electronic device comprising a storage medium, a processor and a computer program stored in said storage medium and executable on said processor, characterized in that said processor executes said computer program to implement the method according to any of claims 1 to 7.
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Application publication date: 20221227