Defect Detection Model for Catenary Components in Environments with Few Samples and Small Targets
［Objective］Aiming at the difficulties of detecting small-volume parts of rail transit catenary under few-sample conditions, a defect detection method integrating generative adversarial networks and deep segmentation models is proposed. ［Method］The composition of the detection system, the improved DC...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Urban Mass Transit Magazine Press
2024-12-01
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| Series: | Chengshi guidao jiaotong yanjiu |
| Subjects: | |
| Online Access: | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.2024.12.015.html |
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| Summary: | ［Objective］Aiming at the difficulties of detecting small-volume parts of rail transit catenary under few-sample conditions, a defect detection method integrating generative adversarial networks and deep segmentation models is proposed. ［Method］The composition of the detection system, the improved DCGAN (deep convolutional generative adversarial network) structure and the improved YOLACT (single-stage conditional adversarial network) model are introduced, and the defect detection effect is verified based on the actual defect datasets. ［Result & Conclusion］In the sample expansion part, the improved DCGAN model can improve the quality of generated samples by introducing several normalization operations and adding an efficient channel attention mechanism. In the model detection part, the improved ResNeXt-FPN (deep con-volutional neural network combining residual network and feature pyramid network) structure is used to replace the backbone network of the original YOLACT model, aiming to fully characterize the multi-scale features of the target. The introduction of CS (channel and space) attention mechanism in the mask branch network can effectively improve the detection accuracy of small-volume parts. The proposed defect detection model can achieve high-precision detection of cotter pins in complex contact network images, with a detection accuracy and recall rate of up to 88.63% and 87.49% respectively. Compared with the original YOLACT model, the comprehensive performance of the proposed defect detection model is improved by about 6.2%. |
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| ISSN: | 1007-869X |