# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 1 Introduction  
At present, closed-circuit television (CCTV) inspection technology is widely employed in sewer inspection tasks. This technique involves workers analyzing videos or images captured by CCTV within the pipelines to identify internal defects in the sewer system. Nevertheless, the extended duration of these inspections can result in visual fatigue, thereby compromising the accuracy of defect identification. Consequently, the utilization of automatic detection methods has been recognized as an effective solution [1; 2; 3; 4; 5]. Numerous methodologies for the detection of defects in sewer systems have been proposed by researchers. [PERSON] et al. [6] introduced an image recognition algorithm for the diagnosis of sewer issues, employing feature extraction and machine learning techniques. This algorithm utilizes a support vector machine (SVM) for the categorization of sewer defects. On a similar note, [PERSON] et al. [7] proposed a machine learning algorithm that utilizes a random forest classifier ensemble to identify different fault types. Despite their effectiveness, both algorithms necessitate manual feature extraction, so their adaptability to the intricate environments encountered in practical engineering applications within sewer systems is limited. [PERSON] et al. [8] utilized convolutional neural networks (CNNs) for the classification of sewer defects. [PERSON] et al. [9] applied a classifier built with InceptionV3 to identify sewer defects. These methods based on single-label classifiers are not well suited for situations where sewer systems exhibit multiple defects simultaneously. The limitation in handling multiple defects underscores the need for further advancements in classification methodologies to address the complexity of real-world sewer environments. In certain studies, researchers have employed semantic segmentation models to address the challenge of identifying multiple defects in sewer systems. [PERSON] et al. [10] introduced a neural network named DilaSeg-CRF, which combines a CNN with denseconditional random fields (CRF) to improve the accuracy in recognizing three types of sewer defects. Similarly, [PERSON] et al. [11] proposed a semantic segmentation network called PipeUNet, designed for the identification of four typical defects. Despite their effectiveness in handling multiple defect types, it is worth noting that the detection speed of both methods is low. This limitation in detection speed may pose challenges, especially in applications where real-time monitoring and rapid analysis are crucial. Further research and development efforts are needed to optimize the efficiency of such semantic segmentation models for faster and more practical sewer defect identification. Furthermore, some researchers have turned to object detection models for sewer defect detection tasks. [PERSON] et al. [12] put forward an automated method based on Regions with CNN Features (R-CNN), designed for high-precision and rapid sewer defect detection. [PERSON] et al. [13] introduced a sewer defect detection model utilizing the Faster R-CNN algorithm, incorporating clustering analysis to effectively enhance the accuracy of defect detection in sewer systems. Despite the high accuracy achieved by these object detection models, there is a limitation in their consideration of on-site equipment performance. Addressing the practicality of these models with on-site tools and conditions is an essential aspect for further research and implementation in real-world sewer inspection scenarios. In recent years, the field of object detection has seen notable advancements, with the You Only Look Once (YOLO) series of algorithms, as proposed by [PERSON] and [PERSON] [14; 15], demonstrating advantages such as high speeds, accuracy, and relatively low computational costs. Addressing the specific application to pipeline defect detection, [PERSON] et al. [16] introduced an improved YOLOv5 algorithm, based on Cycle-GAN. This enhanced algorithm proves effective in identifying defective areas within pipelines.