Efficient detection of fish diseases is essential for intelligent health monitoring and timely intervention in aquaculture. However, current computer vision models remain computationally intensive, hindering their deployment on resource-constrained edge devices in aquaculture applications. To this end, this study developed a lightweight detection framework based on an improved You Only Look Once (YOLO), aiming to achieve a favorable balance between detection accuracy and on-site inference efficiency. First, a Dual-Branch Feature-Preserving Downsampling (DFPD) module was proposed to enhance the extraction of valuable disease-related cues with minimal computational overhead. Subsequently, structured pruning was applied to compress the optimized baseline model. Four pruning techniques, including Slim, GroupTaylor, Layer-Adaptive Magnitude-Based Pruning (LAMP), and L1-based, were evaluated under the same conditions. The enhanced baseline model improved precision from 0.864 to 0.908 and mAP@0.5:0.95 from 0.613 to 0.632, while already reducing the Number of Parameters (Params) and Giga Floating-point Operations Per Second (GFLOPs) compared with the original YOLOv8n. Among the pruning techniques, L1-based produced the best overall trade-off, yielding a final model that maintained a F1-score of 0.860 while reducing Params and GFLOPs by 54.7% and 49.4%, respectively, relative to the original detector. Ablation studies further revealed that a moderate FLOPs reduction of approximately 41% to 47% was optimal for preserving diagnostic performance while enhancing compactness. Edge deployment tests on an RK3588S device verified the framework’s practical inference speed advantage. Therefore, this study offers a deployment-friendly computer vision solution for on-site fish disease detection in aquaculture management, particularly suited to real-world scenarios with limited computational resources.
Li et al. (Sat,) studied this question.