Research on lightweight fish detection algorithm based on improved YOLOv8n

At present, the polar region is used to monitor and investigate fish by combining sonar detection with artificial fishing statistics, which is limited by economic cost, operation area and time. Object detection algorithms based on deep learning can identify and detect fish while meeting economic requirements, but traditional object detection algorithms often have many parameters and calculations and cannot adapt to the harsh conditions of energy consumption and storage limitations in the polar region. To solve this problem, an improved lightweight fish detection algorithm for YOLOv8n was proposed, in which the GhostC2f module was used to replace C2f in the backbone and neck networks, and GhostConv was used to replace part of the Conv in the network, and the EMA attention mechanism was introduced in the backbone network to improve the feature extraction ability. Finally, the MPDIOU loss function, which is simpler in the calculation process, was used to replace the CIOU to improve the detection speed. Experiments on the self-made fish dataset show that the number of parameters and computation of the improved algorithm become 1.49M and 4.7GFLOPs, respectively, and only 49.67% of the parameters of the original YOLOv8n are used to achieve the same detection accuracy, which meets the requirements of model deployment under limited hardware conditions.