基于改进YOLOv5n的轻量化海产生物目标检测OA北大核心CSTPCD

In marine biology research and fishing industries,ships using underwater robots to capture and identify marine organisms may face limited communication bandwidth and computing resources,making lightweight network models better suited for such conditions.To address this issue,a modified YOLOv5n model for marine organism detection was proposed,incorporating improvements in the neck network,model pruning,and knowledge distillation techniques.The model size was effectively reduced by leveraging the Gsconv lightweight convolution module to replace standard convolutions in the YOLOv5n Neck section.A novel α-giou loss function was adopted to enhance bounding box regression accuracy.Based on weight coefficients,L1-norm regularization pruning was applied to eliminate unnecessary channels and associated convolutional kernels.Finally,retraining and L2 knowledge distillation were employed to fine-tune the model accuracy close to pre-pruning levels.Experimental results demonstrated a 53％reduction in computational load and a 51％decrease in parameters compared to the original YOLOv5n baseline network.The proposed algorithm ensures the effectiveness.