Research on the lightweight detection method of rail internal damage based on improved YOLOv8
Abstract To address the challenges of high computational costs, large storage demands, and low detection accuracy in internal rail damage identification, we propose a lightweight detection model, GhostMicroNet-YOLOv8n, as an enhancement of YOLOv8n. This model offers efficient and reliable technical...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
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| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-025-00584-1 |
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| Summary: | Abstract To address the challenges of high computational costs, large storage demands, and low detection accuracy in internal rail damage identification, we propose a lightweight detection model, GhostMicroNet-YOLOv8n, as an enhancement of YOLOv8n. This model offers efficient and reliable technical support for detecting and classifying internal rail damage in rail flaw detection tasks. The proposed model introduces four key innovations. Firstly, the GhostHGNetV2 network is adopted as the feature extraction backbone, which reduces computational costs through structural optimization. Secondly, the Triplet Attention (TA) mechanism is incorporated to enhance the model’s ability to distinguish internal rail damage from complex backgrounds, thereby improving detection accuracy. Thirdly, the network structure is refined to better capture the scale characteristics of damage in B-scan data, improving small-target detection. Finally, the model is compressed using the Layer-Adaptive Sparsity for Magnitude-Based Pruning (LAMP) technique, significantly reducing model complexity while maintaining performance. Experimental results validate the effectiveness of the proposed model, achieving a 2.67% improvement in detection accuracy, a 91.67% reduction in parameters, a 64.20% decrease in computational cost, and an 84.87% reduction in model size compared to YOLOv8n. The model achieves an optimal balance between detection accuracy and computational efficiency, addressing the railroad industry’s demand for real-time defect detection and ensuring accurate flaw identification in resource-constrained Rail Fault Detection (RFD) vehicles. |
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| ISSN: | 1110-1903 2536-9512 |