In marine biology research and fishing industries, ships using underwater robots for capturing and identifying marine organisms may face limited communication bandwidth and computing resources, making lightweight network models a better fit for such conditions.To address this issue, a modified YOLOv5 model for marine organism detection was proposed, incorporating improvements in the neck network, model pruning, and knowledge distillation techniques. By leveraging the Gsconv lightweight convolution module to replace standard convolutions in the YOLOv5n Neck section, the model was effectively reduced. Additionally, a novel α-giou loss function was adopted to enhance bounding box regression accuracy. L1-norm regularization pruning was applied to eliminate unnecessary channels and associated convolutional kernels based on weight coefficients. 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 of lightweight processing while maintaining model performance, offering a promising approach for lightweight handling of marine organism recognition models for underwater robots.