DefectTR: End-to-end defect detection for sewage networks using a transformer

The sanitary sewer is a crucial underground infrastructure of any country that collects wastewater and carries it to the treatment plant. The damage triggered by various factors, such as external interference, long-term corrosion, and uneven distribution of pressure, could lead to various types of defects inside the sewer pipe. Previous studies primarily relied on human visual perception to evaluate the sewage system, which was tedious, time-consuming, and costly. As a result, an efficient and robust sewer defect localization framework was proposed in this manuscript. The main contributions include (1) a novel sewer defect detection system motivated by the state-of-the-art detection transformer (DETR) architecture, which views object localization as a set prediction topic; (2) a defect severity analysis approach based on the transformer's self-attention operation to analyze defect zone of influence and defect grade; and (3) a manually validated sewer defect localization dataset that contains 10 types of commonly appeared sewer defects. The experimental results suggested that the proposed system outperformed the previous standard object detection approaches with the highest mean Average Precision (mAP) of 60.2% on the collected dataset.