Abstract:

[Objective] During the feeding process of fish populations in aquaculture, the video image characteristics of floating extruded feed on the water surface undergo continuous variations due to a myriad of environmental factors and fish behaviors. These variations pose significant challenges to the accurate detection of feed particles, which is crucial for effective feeding management. To address these challenges and enhance the detection of floating extruded feed particles on the water surface, ,thereby providing precise decision support for intelligent feeding in intensive aquaculture modes, the YOLOv11-AP2S model, an advanced detection model was proposed. [Methods] The YOLOv11-AP2S model enhanced the YOLOv11 algorithm by incorporating a series of improvements to its backbone network, neck, and head components. Specifically, an attention for fine-grained categorization (AFGC) mechanism was introduced after the 10th layer C2PSA of the backbone network. This mechanism aimed to boost the model's capability to capture fine-grained features, which were essential for accurately identifying feed particles in complex environments with low contrast and overlapping objects. Furthermore, the C3k2 module was replaced with the VoV-GSCSP module, which incorporated more sophisticated feature extraction and fusion mechanisms. This replacement further enhanced the network's ability to extract relevant features and improve detection accuracy. To improve the model's detection of small targets, a P2 layer was introduced. However, adding a P2 layer may increase computational complexity and resource consumption, so the overall performance and resource consumption of the model must be carefully balanced. To maintain the model's real-time performance while improving detection accuracy, a lightweight VoV-GSCSP module was utilized for feature fusion at the P2 layer. This approach enabled the YOLOv11-AP2S model to achieve high detection accuracy without sacrificing detection speed or model lightweights, making it suitable for real-time applications in aquaculture. [Results and Discussions] The ablation experimental results demonstrated the superiority of the YOLOv11-AP2S model over the original YOLOv11 network. Specifically, the YOLOv11-AP2S model achieved a precision ( P) and recall ( R) of 78.70%. The mean average precision (mAP50) at an intersection over union (IoU) threshold of 0.5 was as high as 80.00%, and the F₁-Score had also reached 79.00%. These metrics represented significant improvements of 6.7%, 9.0%, 9.4% (for precision, as previously mentioned), and 8.0%, respectively, over the original YOLOv11 network. These improvements showed the effectiveness of the YOLOv11-AP2S model in detecting floating extruded feed particles in complex environments. When compared to other YOLO models, the YOLOv11-AP2S model exhibits clear advantages in detecting floating extruded feed images on a self-made dataset. Notably, under the same number of iterations, the YOLOv11-AP2S model achieved higher mAP50 values and lower losses, demonstrating its superiority in detection performance. This indicated that the YOLOv11-AP2S model strikes a good balance between learning speed and network performance, enabling it to efficiently and accurately detect images of floating extruded feed on the water surface. Furthermore, the YOLOv11-AP2S model's ability to handle complex detection scenarios, such as overlapping and adhesion of feed particles and occlusion by bubbles, was noteworthy. These capabilities were crucial for accurate detection in practical aquaculture environments, where such challenges were common and can significantly impair the performance of traditional detection systems. The improvements in detection accuracy and efficiency made the YOLOv11-AP2S model a valuable tool for intelligent feeding systems in aquaculture, as it could provide more reliable and timely information on fish feeding behavior. Additionally, the introduction of the P2 layer and the use of the lightweight VoV-GSCSP module for feature fusion at this layer contributed to the model's overall performance. These enhancements enabled the model to maintain high detection accuracy while keeping computational costs and resource consumption within manageable limits. This was particularly important for real-time applications in aquaculture, where both accuracy and efficiency were critical for effective feeding management. [Conclusions] The successful application of the YOLOv11-AP2S model in detecting floating extruded feed particles demonstrates its potential to intelligent feeding systems in aquaculture. By providing accurate and timely information on fish feeding behavior, the model can help optimize feeding strategies, reduce feed waste, and improve the overall efficiency and profitability of aquaculture operations. Furthermore, the model's ability to handle complex detection scenarios and maintain high detection accuracy while keeping computational costs within manageable limits makes it a practical and valuable tool for real-time applications in aquaculture. Therefore, the YOLOv11-AP2S model holds promise for wide application in intelligent aquaculture management, contributing to the sustainability and growth of the aquaculture industry.

Key words: fish feeding behavior, expanded feed, aquaculture, YOLOv11, real-time detection