Year: 2024
Type: article
Abstract: With the rise of underwater unmanned platforms like unmanned boats, ROVs, and AUVs, there’s an increasing need for underwater detection technologies. Researchers have merged scientific echosounders with these platforms for biometric applications. However, current detection models are too parameter-heavy to embed in echosounders and struggle with noisy, irregular, and dense echograms. This paper introduces YOLOv8-SBE, a lightweight fish detection model based on YOLOv8, addressing these issues by enhancing feature extraction, information fusion, and small object recognition. YOLOv8-SBE adds the C2f_ScConv module to improve efficiency and reduce parameters, incorporates the BiFPN structure to enhance information transfer, and uses the EMA attention module for better small target recognition. It reduces computational complexity by 18.5%, decreases model parameters by 40%, and improves mAP 0.5 to 79.5% and mAP 0.5:0.95 to 58.2%, making it suitable for echosounders with limited resources.
Source: Scientific Reports
Authors: Jungang Ma, Jianfeng Tong, Minghua Xue, Junfan Yao
Institution: Shanghai Ocean University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: gold
Estimated APC paid: $2,590
Award: G440175138
OpenAlex ID: W4405039802