# Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images
###### Abstract  
Drainage pipeline construction projects are vulnerable to a range of defects, such as branch concealed joints, variable diameter, two pipe mouth significances, foreign object insertion, pipeline rupture, and pipeline end disconnection, generated during long-term service in a complex environment. This paper proposes two enhancements to multiple attention learning to detect and segment multiple defects. Firstly, we collected numerous samples of drainage pipeline sonar defect videos. Then, our multiple attention segmentation network was used for target segmentation. The test precision and accuracy of MAP@50 reach 96.0% and 90.9%, respectively, in the segmentation prediction. Compared to the coordinate attention and convolutional block attention module attention models, it had a significant precision advantage, and the weight file size is merely 7.0 MB, which is far smaller than the Yolov9 model segmentation weight size. The multiple attention method proposed in this paper was adopted for detection, instance segmentation, and pose detection in different public datasets, especially in the object detection of the coco128-seg dataset under the same condition. Map@50:95 has increased by 13.0% assisted by our multiple attention mechanism. The results indicated the memory efficiency and high precision of the integration of the multiple attention model on several public datasets. multiple attention learning; enhance attention learning; sonar multi-defect detection; instance segmentation; YoloV11 2020 seriously impede the pipeline's conveying capacity, and in serious cases, these defects can even cause major ground subsidence, jeopardizing people's lives and property. Therefore, it is particularly important to carry out regular defect detection of sewage pipes. Traditional inspection methods include the observation method, the reflector method, and the diving inspection method. The observation method determines whether there are structural defects such as clogging and rupture of the pipeline by manually observing the water level and water quality composition in the manholes between the same pipelines. The reflector method utilizes reflectors to reflect daylight into the interior of the pipeline and observes the clogging and misalignment of the pipeline and other conditions through the mirror. The diving inspection method requires professional diving inspectors to dive into the pipeline to observe and determine whether the pipeline is clogged or misaligned. Although these methods are easy to operate, they are mostly applicable to sewage pipes with large diameters and low water levels, and the scope of the inspection is limited, even requiring inspectors to enter the pipeline to directly observe and record pipeline defects, which is not only inefficient but also poses certain safety risks. At present, the optically based closed-circuit television (CCTV) method is commonly used, where a camera carried by the robot is used. The optical image has a large field of view and high resolution, and the defects can be identified by the image [1; 2]. Some scholars have combined deep learning methods with CCTV inspection methods to greatly improve the inspection efficiency of drainage pipes. [PERSON] et al. provided an efficient self-attention mechanism model for fewer optical image defect classification via a transformer, which enables more effective detection and maintenance of underground drainage systems and reduces the risk of accidents due to sewer pipe defects. [PERSON] et al. proposed a lightweight AutoSewerNet network structure for accurate classification that can prevent system failures in time and prevent environmental pollution caused by sewage leakage, thus safeguarding public health and ensuring the normal operation of urban infrastructures. [PERSON] et al. proposed an automated sewage pipe defect segmentation framework based on DeepLabV3+, which is capable of recognizing the type, location, geometrical information, and severity of the defects. However, optical methods also face many difficulties.