# Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images
###### Abstract
For example, the scattering of turbid water seriously affects the imaging quality. In addition, water absorbs a lot of light, especially in deep water areas, and it is difficult to obtain good images. With the maturity of sonar imaging technology and the development of underwater unmanned detection technology, sonar plays an increasingly important role in underwater target detection. Due to the good characteristics of acoustic waves in underwater media, sonar is also very effective in the field of pipeline defect detection, and it is more effective than cameras or radar [3]. It can provide more accurate and detailed information, even in dark and turbid situations. Furthermore, a sonar image has the advantages of high frequency, long distance, high resolution, multiple beams, and strong real-time performance, and it has been widely used in the field of underwater target detection [4; 5] and segmentation [6; 7]. In previous deep learning target detection networks, the YOLO (You Only Look Once) network framework was widely used for object detection and recognition in drainage pipeline images [8; 9; 10; 11; 12; 13; 14]. [PERSON] [12] adopted lightweight yolo5s and swim transformer networks to detect underwater pipeline sonar target images and achieve high precision. [PERSON] and [PERSON] [15; 16] introduced CycleGAN [17] network or data augmentation to overcome the data-sparsity problem in the sonar domain, and the results improved performance compared to training on solely simulated images. Meanwhile, some researchers applied attention mechanism learning methods to enhance counting or recognition accuracy in sonar images. To boost sonar image-based fish counting accuracy,Duan [18] adopted a novel network with global attention and point supervision, and the results demonstrated that GPNet achieves state-of-the-art performance both in counting accuracy and noise removal. [PERSON] [19] also proposed an instance segmentation model through dynamic attention graph optimization, and the proposed method achieved a performance gain of 4.2% in terms of detection accuracy on ARACATI 2017 datasets. [PERSON] presented a feature pyramid u-net network for forward-looking sonar image semantic segmentation. The method had advantages in small-object and few sample classes [20]. [PERSON] [21] simultaneously proposed a double split attention which fuses spatial attention and channel attention effectively, and the experimental results achieved 78.4% [EMAIL_ADDRESS] on the SCTD dataset. However, little research has been conducted on the detection and segmentation of multi-defects in drainage pipeline sonar based on multiple attention mechanism learning; furthermore, the current segmentation accuracy and efficiency need further improvement to accommodate the deployment constraints of compact autonomous underwater vehicles. A delicate equilibrium must be struck between the size of the weight coefficient and the accuracy obtained during training. This involves a trade-off between the efficiency of the model, which is often inversely proportional to its size, and the precision of predictions it can make, which is influenced by the complexity of the training process. The key objective is to optimize the model's compactness while maintaining acceptable levels of accuracy for practical deployment scenarios. The main problem to be solved in this paper is how to locate and segment target defects in side-scan sonar images more quickly and accurately. Furthermore, there is currently a gap in the literature on measuring the quality of multi-defects in sonar pipe images, and by segmenting and quantifying sonar pipe defects, it is more conductive to assessing and repairing drainage pipeline defect areas. This article is prepared to complete the following tasks. 1. To gather sufficient drainage pipeline multi-defect sonar datasets taken from drainage pipeline ROV inspection equipment for several years.