Automatic detection of sewer defects based on improved you only look once algorithm

Abstract

The drainage system is an important part of civil infrastructure. However, the underground sewage pipe will gradually suffer from defects over time, such as tree roots, deposits, infiltrations and cracks, which heavily affect the performance of sewage pipes. Therefore, it is significant to timely inspect the condition of sewage pipes. Closed-circuit television (CCTV) inspection is a commonly employed underground infrastructure inspection technology requiring engineering experience that can be subjective and inefficient. Nowadays, object detection based on convolutional neural network (CNN) can automatically detect defects, showing high potential for improving inspection efficiency. This paper proposed an improved CNN-based You Only Look Once version 3 (YOLOv3) method for automatic detection of sewage pipe defects, where the improvements are mainly involved in loss function, data augmentation, bounding box prediction and network structure. Experiment results demonstrate that the improved model outperforms Faster R-CNN and YOLOv3, achieving a mean average precision (mAP) value of 92%, which is higher than the existing research on automatic detection of sewage pipe defects.