An Improved YOLOv8n Used for Fish Detection in Natural Water Environments.

Simple Summary: Underwater fish species are an important direction in fishery resource surveys. Rapidly determining species of underwater fish can improve the efficiency of fishery resource surveys. Therefore, this study proposes an effective method for underwater fish measurement, which can quickly acquire underwater fish species. The experimental results demonstrate the accuracy and superiority of our method. The proposed method improves the efficiency of fishery resource surveys and provides crucial data support for the precise management of fishery resources. To improve detection efficiency and reduce cost consumption in fishery surveys, target detection methods based on computer vision have become a new method for fishery resource surveys. However, the specialty and complexity of underwater photography result in low detection accuracy, limiting its use in fishery resource surveys. To solve these problems, this study proposed an accurate method named BSSFISH-YOLOv8 for fish detection in natural underwater environments. First, replacing the original convolutional module with the SPD-Conv module allows the model to lose less fine-grained information. Next, the backbone network is supplemented with a dynamic sparse attention technique, BiFormer, which enhances the model's attention to crucial information in the input features while also optimizing detection efficiency. Finally, adding a 160 × 160 small target detection layer (STDL) improves sensitivity for smaller targets. The model scored 88.3% and 58.3% in the two indicators of mAP@50 and mAP@50:95, respectively, which is 2.0% and 3.3% higher than the YOLOv8n model. The results of this research can be applied to fishery resource surveys, reducing measurement costs, improving detection efficiency, and bringing environmental and economic benefits. [ABSTRACT FROM AUTHOR]