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To address the challenges of limited samples, class imbalance, and real-time requirements in surface defect detection for solar vacuum glass collector tubes, this study proposes an improved lightweight RT-DETR-based method. Specifically, DICM is introduced into the backbone to improve multi-directional and multi-scale feature extraction, HAFB is embedded in the neck to enhance the fusion of local details and global semantics, and transfer learning is adopted to alleviate data scarcity under small-sample conditions. Experiments on a self-built defect dataset of solar vacuum glass collector tubes show that the proposed method outperforms the original RT-DETR and several mainstream detectors in terms of Precision, Recall, mAP@0.5, and F1-score while maintaining favorable inference speed and model compactness. Under the same hardware conditions, the proposed model achieves an mAP@0.5 of 0.95, an inference speed of 83.21 FPS, and a model size of 82.36 MB. These results demonstrate the feasibility of the proposed method for real-time online defect detection in industrial scenarios.
Xiao et al. (Wed,) studied this question.