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Metal pipelines may have various defects due to long-term service, corrosion, external strikes, etc. Traditional closed-circuit television (CCTV) inspection techniques are capable of detecting these defects. However, substantial human resources are required and the detection results are subjected to human subjectivity. Thus, this study develops a deep learning-based intelligent defect detection model for metal pipeline images. CNNs (convolutional neural networks) are utilized to automatically extract defects, which may mitigate the interference of subjective factors and enhance the recognition capability of the defects in pipelines. The proposed model builds upon the original YOLOv8n model by incorporating the SCSA (Spatial and Channel Synergistic Attention) mechanism, LskBlock, and SlideLoss function, respectively. These enhancements improve the ability to detect small targets, increase recognition accuracy, and facilitate global optimization, respectively. The developed YOLO-LSS (YOLOv8n-LskBlock-SlideLoss-SCSA) model is compared with other deep learning models characterized by the following metrics: mAP50, mAP50:95, precision, recall rate, and F1-score, respectively. It is found that mAP50 achieves 79.05% (+2.86%), mAP50:95 53.7% (+7.19%), precision 81.6% (+5.30%), recall rate 75.5% (+2.90%), and F1-score 78.4 (+4.01), indicating that the proposed model effectively enhances the capability of detecting internal defects in pipelines.
Zheng et al. (Thu,) studied this question.