Side-Scan Sonar Image Detection of Shipwrecks Based on CSC-YOLO Algorithm
Shengxi Jiao1, Fenghao Xu1, Haitao Guo2,*
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
* Corresponding Author: Haitao Guo. Email: 
(This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057192
Received 10 August 2024; Accepted 21 November 2024; Published online 17 December 2024
Abstract
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 computational complexity decreases by 23.2%, and Frames Per Second (FPS) increases by 17.6%. In addition, compared with the advanced popular model, the superiority of the proposed model is proved. The subsequent experiments on real side-scan sonar images of shipwrecks fully demonstrate that the CSC-YOLO algorithm meets the requirements for actual side-scan sonar detection of underwater shipwrecks.
Keywords
Enhanced YOLOv8; side-scan sonar; shipwreck detection; group normalization; deep learning
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