# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 1 Introduction  
Sewer systems play an important part in urban infrastructure and usually have a long service life. However, with the increase in use time, various defects will appear in sewer pipes, such as deposition, stagger and crack. If these defects cannot be found and dealt with in a timely manner, they will seriously reduce the lifetime of the sewer system and threaten urban safety. Studies have found that flooding events in cities can be caused not only by extreme weather but also by blockages and collapses in sewer pipes [1]. Therefore, it is necessary to conduct thorough and regular inspections of the sewer pipes, and then take appropriate measures to deal with these defects. Currently, closed-circuit television (CCTV) inspection technology has been widely used for the inspection of sewer pipes [2]. CCTV uses a robot with a camera to enter the sewer pipes for video shooting, and then provide the collected video data to professional technicians for evaluation. Although CCTV greatly improves safety by not requiring a human to enter the sewer pipe, professional technicians are still required to inspect the video in detail, which is labor intensive, inefficient and difficult to guarantee accuracy [3]. Moreover, due to the large number of sewer pipes and the limited number of professional technicians [4], it is difficult to conduct a comprehensive inspection of sewer pipes in time. Therefore, there is an urgent need to develop an automatic sewer defect detection method, which can not only speed up the detection process but also eliminate the potential human bias of technicians [5]. In addition, automatic detection technology does not require a constant concentration of technicians and can detect some minor and imperceptible defects in time, such as cracks and fractures [6]. Due to the shortcomings of manual inspection, automated detection methods based on computer vision (CV) techniques and deep learning techniques are gradually developed in the field of sewer defect detection. CV-based techniques focus on accurately designing feature information used to describe pipe defects, such as texture features and shape features of the image. However, the process is complicated, inefficient and requires technicians to design features manually, which depends on the experience level of technicians. In recent years, deep learning techniques have achieved better results in various computer vision tasks such as image classification and object detection. By simply feeding a large amount of raw data into the deep learning network, the complex network structure can automatically extract feature information of the target, which greatly improves the accuracy and efficiency of detection. Image classification technology can recognize a single image, but cannot determine the specific location of the target. Object detection technology can detect multiple targets in a single image at the same time and can detect the specific location of the target, which is more suitable for actual sewer defect detection tasks. Therefore, this paper chooses object detection technology for sewer defect detection research. Specifically, this paper proposes an improved YOLOv4 model for automatic detection of sewer pipeline defects, which achieves accurate detection and recognition of four types of defects. Due to the large differences in morphology and size among different sewer defects, small defects are easy to lose feature information in the detection process. Moreover, object detection networks usually pay more attention to the local features of the target and not enough to the global features of the image, which leads to poor classification performance of the target. Therefore, this paper introduces the spatial pyramid pooling (SPP) module to achieve fusion of defect feature information at different scales, enabling models to obtain richer global and local information. This method is simple and efficient, does not cause a large amount of computation, and can effectively improve the defect detection accuracy, especially for small defects.