Vehicle loss detection method and device, electronic equipment and storage medium

A detection method and vehicle technology, applied in the fields of devices, electronic equipment and storage media, and vehicle loss detection methods, can solve problems such as inaccuracy, achieve good feature extraction and utilization, a wide range of vehicle models, and effectively locate and identify damaged parts. Effect

Pending Publication Date: 2021-11-16
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the process of image analysi

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  • Vehicle loss detection method and device, electronic equipment and storage medium
  • Vehicle loss detection method and device, electronic equipment and storage medium
  • Vehicle loss detection method and device, electronic equipment and storage medium

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Experimental program
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Embodiment 1

[0024] figure 1 It is a flow chart of the vehicle loss detection method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of vehicle loss detection. The method can be executed by electronic equipment, which can be a computer device or a terminal, and specifically includes the following steps :

[0025] Step 110, acquiring a target image.

[0026] The target image is the image for vehicle loss detection. The user can take pictures of the damaged vehicle through the handheld terminal, and use the pictures taken as the target image. It is also possible to import a pre-captured image to a computer device as a target image.

[0027] Step 120, input the target image into the network model, the backbone network of the network model includes the SwinTransformer network backbone network for predicting the damage position coordinates and damage category of the target image based on the Swin Transformer network.

[0028] The structure d...

Embodiment 2

[0050] Figure 4 The flow chart of the vehicle loss detection method provided by Embodiment 2 of the present invention, as a further description of the above embodiment, further includes a step of training the Swin Transformer network before acquiring the target image in step 110 . Embodiment 1 provides an implementation manner in which a Swin Transformer network is used as a backbone network for vehicle damage detection. Embodiment 2 is used to provide a training method for the above-mentioned network. This method can be implemented by:

[0051] Step 210, mark the historical pictures of car damage according to the labeling criteria, and configure the damage categories of the historical pictures of car damage.

[0052] Among them, the damage category and labeling criteria can be determined by the damage assessment personnel and algorithm engineers. The damage categories include varying degrees of severity of vehicle damage for which compensation is required. The labeling c...

Embodiment 3

[0072] Figure 5 It is a schematic structural diagram of a vehicle loss detection device provided in Embodiment 3 of the present invention. This embodiment is applicable to the situation of vehicle loss detection. The method can be executed by electronic equipment, which can be a computer device or a terminal, and specifically includes: image An acquisition module 310 , a detection module 320 and a detection result determination module 330 .

[0073] An image acquisition module 310, configured to acquire a target image;

[0074]The detection module 320 is used to input the target image into the network model, the backbone network of the network model includes a Swin Transformer network, and the backbone network is used to predict the damage position coordinates and damage category of the target image based on the Swin Transformer network;

[0075] The detection result determination module 330 is configured to determine the damage detection result according to the damage posit...

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Abstract

The invention discloses a vehicle loss detection method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target image; inputting the target image into a network model, a backbone network of the network model including a Swin Transformer network, the backbone network being used for predicting a damage position coordinate and a damage category of the target image based on the Swin Transformer network; and determining a damage detection result according to the damage position coordinate and the damage category. According to the embodiment of the invention, the Swin Transformer network is used as the backbone network, so that compared with a CNN detection mode, the method is more accurate, and can more effectively positions and identifies a damaged part. The Swin Transformer is used as the backbone network to extract features, so that spatial information relation among pixels of the image and weighted selection of the features can be explored, and better feature extraction and utilization are realized. In addition, the Swin Transformer has the characteristics of locality, translation invariance, residual learning and the like of the CNN, so that the problems of complicated calculation amount and large memory consumption in other visual Transformer schemes can be solved while the performance of the Swin Transformer method exceeds that of the CNN method.

Description

technical field [0001] Embodiments of the present invention relate to machine learning technology, and in particular to a vehicle loss detection method, device, electronic equipment, and storage medium. Background technique [0002] With the rapid development of society, vehicles have become one of the indispensable means of transportation, and the increasing number of vehicles undoubtedly increases the incidence of traffic accidents. After a traffic accident, the insurance company usually goes to the accident scene to determine the damage, that is, to determine the vehicle damage by observing the photos taken at the scene, and use it as the basis for the auto insurance company's claims. Because the link of loss determination consumes a lot of human resources, and the results obtained are highly subjective. Therefore, the vehicle damage detection system based on the deep learning method gradually replaces the manual operation, which can accurately detect the type of vehicle...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/241
Inventor 康甲刘莉红刘玉宇
Owner PING AN TECH (SHENZHEN) CO LTD
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