Pear flower recognition in natural environment based on improved YOLOv5s
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.
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