To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to e...To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to enhance detection precision.Building upon the YOLO11n framework,the proposed improvements include three key components:1)SimCSPSPPF in Backbone:An improved Spatial Pyramid Pooling-Fast(SPPF)module,SimCSPSPPF,is integrated into the Backbone to reduce the number of channels in the hidden layers,thereby accelerating model training.2)PEC in Neck:The standard convolution layers in the Neck are replaced with Perception Enhancement Convolutions(PEC)to improve multi-scale feature extraction capabilities,enhancing detection speed.3)AWIoU Loss Function:The regression loss function is replaced with Adequate Wise IoU(AWIoU),addressing issues of bounding box distortion caused by the diversity in pest species and size variations,thereby improving the precision of bounding box localization.Experimental evaluations on the IP102 dataset demonstrate that PSA-YOLO11n achieves a mean Average Precision(mAP)of 89.10%,surpassing YOLO11n by 0.8%.Comparisons with other mainstream algorithms,including Faster R-CNN,RetinaNet,YOLOv5s,YOLOv8n,YOLOv10n,and YOLO11n,confirm that PSA-YOLO11n outperforms all baselines in terms of detection performance.These results highlight the algorithm’s capability to significantly improve the detection accuracy of multi-scale wheat pests in natural environments,providing an effective solution for pest management in wheat production.展开更多
文摘To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to enhance detection precision.Building upon the YOLO11n framework,the proposed improvements include three key components:1)SimCSPSPPF in Backbone:An improved Spatial Pyramid Pooling-Fast(SPPF)module,SimCSPSPPF,is integrated into the Backbone to reduce the number of channels in the hidden layers,thereby accelerating model training.2)PEC in Neck:The standard convolution layers in the Neck are replaced with Perception Enhancement Convolutions(PEC)to improve multi-scale feature extraction capabilities,enhancing detection speed.3)AWIoU Loss Function:The regression loss function is replaced with Adequate Wise IoU(AWIoU),addressing issues of bounding box distortion caused by the diversity in pest species and size variations,thereby improving the precision of bounding box localization.Experimental evaluations on the IP102 dataset demonstrate that PSA-YOLO11n achieves a mean Average Precision(mAP)of 89.10%,surpassing YOLO11n by 0.8%.Comparisons with other mainstream algorithms,including Faster R-CNN,RetinaNet,YOLOv5s,YOLOv8n,YOLOv10n,and YOLO11n,confirm that PSA-YOLO11n outperforms all baselines in terms of detection performance.These results highlight the algorithm’s capability to significantly improve the detection accuracy of multi-scale wheat pests in natural environments,providing an effective solution for pest management in wheat production.
