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Open Access

ARTICLE

Lightweight Underwater Target Detection Using YOLOv8 with Multi-Scale Cross-Channel Attention

Xueyan Ding1,2, Xiyu Chen1, Jiaxin Wang1, Jianxin Zhang1,2,*
1 School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116600, China
2 Research Center of Multimodal Information Perception and Intelligent Processing, Dalian Minzu University, Dalian, 116600, China
* Corresponding Author: Jianxin Zhang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057655

Received 23 August 2024; Accepted 23 October 2024; Published online 11 November 2024

Abstract

Underwater target detection is extensively applied in domains such as underwater search and rescue, environmental monitoring, and marine resource surveys. It is crucial in enabling autonomous underwater robot operations and promoting ocean exploration. Nevertheless, low imaging quality, harsh underwater environments, and obscured objects considerably increase the difficulty of detecting underwater targets, making it difficult for current detection methods to achieve optimal performance. In order to enhance underwater object perception and improve target detection precision, we propose a lightweight underwater target detection method using You Only Look Once (YOLO) v8 with multi-scale cross-channel attention (MSCCA), named YOLOv8-UOD. In the proposed multi-scale cross-channel attention module, multi-scale attention (MSA) augments the variety of attentional perception by extracting information from innately diverse sensory fields. The cross-channel strategy utilizes RepVGG-based channel shuffling (RCS) and one-shot aggregation (OSA) to rearrange feature map channels according to specific rules. It aggregates all features only once in the final feature mapping, resulting in the extraction of more comprehensive and valuable feature information. The experimental results show that the proposed YOLOv8-UOD achieves a mAP50 of 95.67% and FLOPs of 23.8 G on the Underwater Robot Picking Contest 2017 (URPC2017) dataset, outperforming other methods in terms of detection precision and computational cost-efficiency.

Keywords

Deep learning; underwater target detection; attention mechanism

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