【摘要】 针对梨花密集、遮挡严重、目标太小导致的召回率低的问题，本文提出基于改进YOLOv5s的自然环境下梨花识别方法。该方法首先添加了小目标检测层，通过增加CSPDarknet主干特征提取网络的浅层输出特征层，以及在PANet加强特征提取网络中对该浅层特征层进一步特征融合，增大了对浅层特征和细节信息的提取能力。其次，在PANet网络中引入了CBAM注意力模块，提高了对重要特征的表达能力。结果表明，本文改进的YOLOv5s-P-CBAM网络模型能够有效降低漏识别率，改进后模型的精确率、召回率、F1值、mAP分别为91.62%、83.05%、87.12%、94.06%，相比原模型分别提高了0.16%、1.55%、0.93%和0.61%。此外，对‘雪青’、‘鸭梨’和‘秋月’3个品种的梨花图像均能实现较好的识别效果，具有较强的泛化性，为梨园的机器智能疏花提供了技术支持。更多还原

【Abstract】 This article proposed a pear blossom recognition method based on improved YOLOv5s in natural environments to address the low recall rate caused by dense pear blossoms, severe occlusion, and small targets. This method first added a small target detection layer. The small target detection layer was a shallow output feature layer added to the CSPDarknet backbone feature extraction network, and further feature fusion was performed on this shallow feature layer in the PANet enhanced feature extraction network. This enhanced the ability to extract shallow features and detailed information. Secondly, the CBAM attention module was introduced into the PANet network to improve the expression ability of important features. The experimental results showed that the improved YOLOv5s network model in this paper could reduce the missed recognition rate. The accuracy, recall, F1 value, and mAP of the improved model were 91.62%, 83.05%, 87.12%, and 94.06%, respectively, which were 0.16%, 1.55%, 0.93%,and 0.61% higher than that of the original model. In addition, better recognition results could be achieved on pear flower images of three varieties: Xueqing, Yali, and Qiuyue. This model has strong generalization ability and provides technical support for machine intelligent thinning of pear orchards.更多还原