Transfer Learning for Vehicle Damage Segmentation
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Solution Overview
Problem
Current methods for pixel-level damage segmentation in vehicle damage images are inefficient due to the complexity of irregular shapes and high costs associated with manual labeling, making it difficult to accurately determine damage boundaries.
Innovation Solution
A damage segmentation model based on transfer learning is developed, utilizing a combination of segmentation and detection samples to train a model that can accurately outline damage objects in vehicle images, leveraging similarity between component and damage object features to improve segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to determine pixel-level damage regions, then accuracy of damage object identification can be improved, but the complexity and time consumption increase significantly due to irregular shapes and discontinuous boundaries
Solution Approach 1:
The patent uses a pre-trained recognition model to generate automatic labeling results that copy the desired segmentation output. The model learns from training samples and reproduces accurate damage region boundaries without requiring manual intervention for each new image, thus resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The recognition model is pre-trained offline using a large number of labeled training samples. This preliminary action prepares the model in advance, so that during actual deployment, the model can quickly and accurately segment damage regions without requiring real-time manual labeling, thereby reducing both complexity and time consumption while maintaining high accuracy.
2Measurement precision
If manual labeling is used for pixel-level damage segmentation, then precise boundary determination can be achieved, but the time consumption and productivity are significantly reduced
Solution Approach 1:
The pre-trained recognition model automatically generates segmentation results by copying the labeling pattern learned from training data. This allows the system to produce precise boundary determinations at automated speeds, eliminating the time-consuming manual process while maintaining high precision through the model's learned features.
Solution Approach 2:
The patent transforms the segmentation task from a manual parameter-adjustment process to an automated model inference process. By changing from manual control parameters to model-learned parameters, the system achieves both high precision (through learned features) and high productivity (through automated processing without manual intervention).
3Measurement precision
If a recognition model is trained with pixel area labeling, then accurate damage part recognition can be achieved, but the labeling cost and time investment increase
Solution Approach 1:
The patent performs the time-consuming labeling work in advance during the model training phase. A dataset is labeled once to train the recognition model, and then the model can automatically process numerous new images without requiring additional manual labeling. This preliminary action converts one-time labeling effort into ongoing automated accuracy, resolving the contradiction between precision and labeling time.
4Measurement precision
If manual labeling is used for irregular damage shapes, then comprehensive damage coverage can be ensured, but the operation complexity and difficulty increase
Solution Approach 1:
The recognition model automatically copies the correct segmentation patterns for various damage types and shapes from training data. This eliminates the need for operators to manually handle complex irregular shapes, as the model has already learned to identify and segment them accurately during training, thus improving ease of operation while maintaining comprehensive damage coverage.
Data Source
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AI summary
Embodiments of the specification provide a transfer learning based method for performing component segmentation and damage segmentation. Specifically, in a first aspect, a transfer method can be learned on the basis of high-frequency component classifications having rich detection and segmentation data, and then for low-frequency component classifications having only detection data but not having rich segmentation data, a detection box thereof can be applied to this transfer method to obtain a corresponding segmentation result; in a second aspect, a detection to segmentation transfer method can be learned on the basis of high-frequency damage classifications having rich detection and segmentation data, and then for low-frequency damage classifications having only detection data but not having rich segmentation data, a detection box thereof can be applied to this transfer method to obtain a corresponding segmentation result; in a third aspect, a damage segmentation result can be obtained by means of component to damage cross-domain transfer learning. These methods can solve the problem of difficulty in acquiring segmentation data of long-tailed component classifications and damage classifications.