Research on Improved Lightweight Fish Detection Algorithm Based on Yolov8n

Abstract

The fish detection algorithm is of great significance for obtaining aquaculture information, optimizing prey allocation in aquaculture, and improving the growth efficiency and survival rate of fish. To address the challenges of high complexity, large computational load, and limited equipment resources in existing fish target detection processes, a lightweight fish detection and recognition method based on the Yolov8 network, called the CUIB-YOLO algorithm, is proposed. This method introduces a secondary innovative C2f-UIB module to replace the original C2f module in the Yolov8 neck network, effectively reducing the model’s parameter count. Additionally, the EMA mechanism is incorporated into the neck network to enhance the feature fusion process. Through optimized design, the Params and FLOPs of the CUIB-YOLO algorithm model are reduced to 2.5 M and 7.5 G, respectively, which represent reductions of 15.7% and 7.5% compared to the original YOLOv8n model. The mAP @ 0.5–0.95/% value reaches 76.4%, which is nearly identical to that of the Yolov8n model. Experimental results demonstrate that compared with current mainstream target detection and recognition algorithms, the proposed model reduces computational load without compromising detection accuracy, achieves model lightweighting, improves inference speed, and enhances the algorithm’s real-time performance.

Keywords

• machine vision
• fish identification and detection
• yolov8n model
• lightweight network