基于改进YOLO11的海马细菌性肠炎检测方法OA

To address the challenge of accurately detecting signs of bacterial enteritis in cultured seahorses,this study constructs a dedicated underwater seahorse image dataset and proposes YOLO11-SH,a lightweight detection model based on an improved YOLO11.The study first evaluates the impact of various image enhancement algorithms on detection performance and finds that directly applying enhancement preprocessing does not universally improve performance for object detection.Therefore,this study focuses on targeted optimizations of the YOLO11 network architecture.Specifically,it introduces multi-scale dilated attention(MSDA)and multi-scale channel attention(MSCA)mechanisms to enhance fine-grained feature extraction capabilities for small targets in complex underwater environments.A dynamic upsampling(DySample)strategy was adopted to adaptively adjust sampling weights and effectively recover shallow spatial details.Additionally,the Shape-IoU loss function is integrated to improve bounding box regression and localization accuracy for the elongated morphology of seahorses.Experimental results demonstrate that with a parameter size of only 2.9M,YOLO11-SH achieves an mAP@50 of 84.3%,mAP@50-95 of 63.6%,and a recall rate of 77.6%,representing improvements of 1.2%,1.7%,and 2.0%over the baseline model,respectively.Furthermore,robustness tests prove that the model maintains stable detection performance under non-ideal underwater degradation conditions,such as motion blur and Gaussian noise.This study provides efficient and feasible technical support for intelligent health monitoring and early disease warning in seahorse aquaculture.