Side-Scan Sonar Image Detection of Shipwrecks Based on CSC-YOLO Algorithm  

作　　者：Shengxi Jiao Fenghao Xu Haitao Guo 

机构地区：[1]School of Automation Engineering,Northeast Electric Power University,Jilin,132012,China [2]College of Marine Science and Technology,Hainan Tropical Ocean University,Sanya,572022,China

出　　处：《Computers, Materials & Continua》2025年第2期3019-3044,共26页计算机、材料和连续体(英文)

基　　金：supported in part by the Hainan Provincial Natural Science Foundation(Grant No.420CXTD439);Sanya Science and Technology Special Fund(Grant No.2022KJCX83);Institute and Local Cooperation Foundation of Sanya in China(Grant No.2019YD08);National Natural Science Foundation of China(Grant No.61661038).

摘　　要：Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the task of object detection in shipwreck side-scan sonar images, due to the complex seabed environment, it is difficult to extract object features, often leading to missed detections of shipwreck images and slow detection speed. To address these issues, this paper proposes an object detection algorithm, CSC-YOLO (Context Guided Block, Shared Conv_Group Normalization Detection, Cross Stage Partial with 2 Partial Convolution-You Only Look Once), based on YOLOv8n for shipwreck side-scan sonar images. Firstly, to tackle the problem of small samples in shipwreck side-scan sonar images, a new dataset was constructed through offline data augmentation to expand data and intuitively enhance sample diversity, with the Mosaic algorithm integrated to strengthen the network’s generalization to the dataset. Subsequently, the Context Guided Block (CGB) module was introduced into the backbone network model to enhance the network’s ability to learn and express image features. Additionally, by employing Group Normalization (GN) techniques and shared convolution operations, we constructed the Shared Conv_GN Detection (SCGD) head, which improves the localization and classification performance of the detection head while significantly reducing the number of parameters and computational load. Finally, the Partial Convolution (PConv) was introduced and the Cross Stage Partial with 2 PConv (C2PC) module was constructed to help the network maintain effective extraction of spatial features while reducing computational complexity. The improved CSC-YOLO model, compared with the YOLOv8n model on the validation set, mean Average Precision (mAP) increases by 3.1%, Recall (R) increases by 6.4%, and the F1-measure (F1) increases by 4.7%. Furthermore, in the improved algorithm, the number of parameters decreases by 20%, the computa

关 键 词：Enhanced YOLOv8 side-scan sonar shipwreck detection group normalization deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]