Abstract
High-precision fish detection is the fundamental prerequisite for automated counting in aquaculture. However, current research lacks lightweight yet highly accurate detection models specifically designed to address occlusion challenges in high-density scenarios within controlled environments. To address this deficit, a novel lightweight fish detection model was constructed, which signifies the adaptation of the YOLO (You Only Look Once) framework, optimized specifically for enhancing detection performance under counting-oriented conditions. This model has been named YOLO-FC (YOLO constructed specifically for Fish Counting Applications). In YOLO-FC, the backbone network is significantly streamlined through the integration of a new feature extraction module and the use of SAC (Switchable Atrous Convolution). Simultaneously, the neck network’s feature fusion approach is revamped with a weighted feature fusion method. Additionally, the model introduces improved EIOU (Efficient Intersection over Union) into the BBR (Bounding Box Regression) loss function. Following the evaluation of different detection head combinations and feature extraction modules, the final model utilizes a single detection head, with parameter count and computational demands representing only 14.7% and 73.2% respectively compared to YOLOv5 nano. Experimental results on the self-built fish dataset showed that the nano YOLO-FC achieved a detection P (precision) of 97.9%, R (recall rate) of 97.2%, and AP50 (Average Precision at Intersection over Union threshold of 0.50) of 98.8%. These metrics surpass those of mainstream object detection models and existing fish detection models. Furthermore, to verify generalizability, the model was evaluated on a shrimp larvae dataset, demonstrating robust detection capabilities across different aquatic species. The proposed model provides a solid technological foundation for the detection stage in high-density counting systems.