Detection of peach trees in unmanned aerial vehicle(UAV)images based on improved Fas-ter-RCNN network
Studying precise detection and positioning methods for peach trees can provide support for precision man-agement of peach orchards.This study utilizes unmanned aerial vehicle(UAV)remote sensing combined with deep learning algorithms to detect leafless peach trees and differentiate between budding and flowering peach trees.Three improvements are proposed based on the Faster R-CNN original network:replacing the backbone network with a ResNeXt-50 integrated with a convolutional block attention module(CBAM),using ROI Align instead of RO1 Poo-ling for feature extraction from regions of interest,and introducing the Focal Loss function for imbalanced cross-entro-py loss.Ablation experiments are conducted to analyze the effectiveness of these improvements.The experiments demonstrate that compared with the unimproved Faster R-CNN,the improved model achieves a mean average preci-sion(mAP)increase of 8.95 percentage points,reaching 86.46％,enabling better differentiation between flowering and budding peach trees.The most significant improvement contributing to the model's enhancement is the replace-ment of the ResNeXt-50 backbone network with CBAM,resulting in a 5.98 percentage points increase in mAP;the use of ROI Align reduces errors in the feature quantization process,leading to a 2.33 percentage points increase in mAP.Compared with other mainstream detection models such as YOLOv3,YOLOv5x,and SSD,the proposed model in this study demonstrates superior detection performance.The UAV remote sensing combined with the improved Fas-ter R-CNN algorithm proposed in this study can effectively detect peach trees,meeting the requirements of precision management in peach orchards.
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