基于改进YOLO v8n的玉米病害检测系统OA

Aiming to address the high missed detection rate and poor robustness of the YOLO v8n target detection model when identifying maize diseases under significant lighting changes and background disorder,a novel maize disease identification model was proposed based on improved YOLO v8n,DSEMA-YOLO.Based on YOLO v8n,by integrating the EMA global attention mechanism before the C2f module bottleneck,a C2f-attention module was constructed,effectively reducing interference from complex backgrounds on defective image recognition while fully capturing contextual information.By adding the DSConv module to reconstruct the neck network structure and adopting an adaptive convolutional kernel shape adjustment mechanism,it can more accurately capture target features of corn diseases in the image,mitigating the effects of lighting changes and background clutter.Finally,by introducing a 160 pixel×160 pixel scale feature fusion layer to optimize the neck network structure,the detection accuracy of small object bounding boxes was improved,and the fusion of shallow and deep information was strengthened,thereby improving recognition accuracy.A micro-object detection module was added to the head network,connected to the 160 pixel×160 pixel scale feature fusion layer.In the experiment,the improved model mAP@0.5 of the maize disease dataset collected in the field environment was 92.2％,an improvement of 7.7 percentage points over that of the YOLO v8n model.While maintaining detection speed and low computational overhead,it effectively improved the accuracy of maize disease identification.