针对自然环境中小麦害虫种类繁多、尺寸差异大和生长环境复杂导致检测精度低的问题，提出了一种PSA-YOLO11n小麦害虫检测算法，以提升小麦害虫的检测精度。在 YOLO11n 算法的基础上，对三个关键组成部分进行改进：1)在主干部分引入一种改进空间金字塔池化SimCSPSPPF(Sim CSP Spatial Pyramid Pooling - Fast，SimCSPSPPF) 模块，降低隐藏层的通道数量，加快模型训练速度。2)在中间部分将普通卷积替换为效果更好的感知增强卷积（Perception enhancement convolution，PEC），增强多尺度特征提取能力，提升目标检测速度。 3)回归损失函数更换为AWIoU（Adequate Wise IoU ），改善害虫种类繁多、尺寸差异大造成的检测框失真，提升边界框定位能力。利用IP102数据集进行试验验证，PSA-YOLO11n 与 YOLO11n 相比，mAP提升0.8%，达到89.10%。与Faster R-CNN、RetinaNet、YOLOv5s、YOLOv8n 、YOLOv10n和 YOLO11n 等主流算法进行比较，模型性能均优于其它对比算法。试验结果表明，改进算法PSA-YOLO11n，显著提升了自然环境下多尺度小麦害虫检测精度，为小麦病虫害防治提供一种有效的解决方案。

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.