Drainage Pipeline Defect Detection System Based on Semantic Segmentation

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Since drainage pipes are widely used in daily life and are necessary for cities to function normally, intelligent fault detection in these pipes has emerged as both an urgent necessity and a major area of development. Compared to traditional target detection methods, the semantic segmentation-based pipeline defect detection method has some special qualities. It can effectively detect a variety of pipeline defect types and segment the defects using precise geometrical attributes to support subsequent defect assessment. This study replaces the backbone network and fuses bar pooling and cascade feature fusion modules in the encoding and decoding phases, respectively, to obtain richer semantic information. This approach strikes a balance between segmentation accuracy and model complexity, aiming to address the problems of the large computational volume of semantic segmentation and the loss of detailed semantic information of pipeline defects. In the pipe defect dataset, the proposed approach is compared with the baseline algorithm and the experimental findings indicate that it has a greater segmentation accuracy than the conventional algorithm. Additionally, the lightweight design of the algorithm lowers the model’s complexity.