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A sewer pipe system is an indispensable part of a modern city’s infrastructure. However, sewer pipes gradually deteriorate over time and develop defects such as cracks, deposits, and infiltration, which seriously affect their performance. Consequently, routine inspections are crucial for assessing the condition of sewer pipes and ensuring their proper operation. Closed-circuit television (CCTV) is frequently utilized for sewer pipe inspection. Previously, the interpretation of sewer pipe CCTV images relied on professional technologists and traditional computer vision techniques to identify and localize defects. They are inefficient and poorly robust. In this paper, we propose an efficient and robust automatic interpretation technique based on deep learning to identify and localize defects in sewer pipe CCTV images in real time. Specifically, we propose an improved you only look once version 5 large 6 (YOLO v5l6) network based on the original YOLO v5l6 network. The improved YOLO v5l6 network is based on improvements in the following two aspects: (1) We concatenate a convolutional block attention module (CBAM) module into the Bottleneck block of the Cross Stage Partial Bottleneck with 3 Convolutions (C3) module at the end of the original YOLO v5l6 backbone to create a C3CBAM module. (2) Moreover, we concatenate the CBAM into the end of four C3 modules of the original YOLO v5l6 neck to create four C3_CBAM_Att modules. With these improvements, the improved YOLO v5l6 network can significantly reinforce the backbone feature extraction and neck feature fusion to rapidly and accurately detect multiscale, multiclass defects in the presence of interference. |
