SDH-FCOS: An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last output feature layer of the backbone network, fused the local and global features, added a top-down path to accelerate the circulation of shallow information, and enriched the semantic information acquired by shallow features. The ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection heads. The experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately; the average accuracy was improved by 2.7% compared with the original FCOS model, reducing the leakage rate to a large extent and achieving real-time detection. Also, our model achieved a good trade-off between accuracy and speed compared with other mainstream methods. This proved the effectiveness of the proposed model.