基于CAI-YOLO算法的鲫鱼病害图像识别方法OA

To address the challenges of low detection accuracy and high false positive rates caused by the complex morphology,significant scale variations,and blurred boundaries of lesion areas in Carassius auratus diseases,this paper proposes a novel recognition model named CAI-YOLO based on the YOLOv11 framework.First,the backbone network incorporates the ConvNeXt V2 module.This module utilizes a self-supervised pre-training strategy based on Masked Auto Encoders and introduces a Global Response Normalization layer,effectively mitigating feature collapse and enhancing feature diversity.Second,the neck network integrates AKConv,which leverages an adaptive sampling mechanism to improve the model's multi-scale modeling capability for irregular disease spots.Finally,the loss function employs IF-IOU,which combines the internal constraints of Inner-IOU with the re-weighting mechanism of Focaler-IOU,thereby accelerating model convergence and improving localization accuracy.Experiments conducted on a self-built Carassius auratus disease dataset show that the CAI-YOLO model achieves Precision,Recall,mAP@0.5,and mAP@0.5:0.95 of 85.6％,87.8％,86.7％,and 58.6％,respectively.Compared to the baseline YOLOv11n,the mAP@0.5 and mAP@0.5:0.95 are increased by 0.9 and 1.1 percentage points,respectively.Furthermore,the number of parameters,computational complexity,and model size are reduced by 10.89％,8.19％,and 7.84％,respectively.The research demonstrates that the CAI-YOLO model effectively enhances overall detection performance while simultaneously reducing computational resource requirements,providing a valuable reference for the lightweight design and practical application of Carassius auratus disease detection systems.