Abstract:

Individual tree detection plays a critical role in urban ecosystem management and greening monitoring.While deep learning algorithms are widely applied in tree detection due to their strong performance in computer vision,most methods struggle to achieve precise detection in complex urban environments,especially with varying crown sizes,diverse species,and high canopy density.To address these challenges,this paper proposes an improved YOLOv5-based algorithm,YOLOv5-CSC,for detecting camphor trees in unmanned aerial vehicle(UAV RGB)images.The model uses diverse convolution kernels in the ConvNeXt module for fine-grained local feature extraction,and incorporates SPPF and C3TR modules to enhance the receptive field and feature fusion efficiency,significantly improving crown detection in complex urban settings.Experimental results show that the YOLOv5-CSC model achieves mean average precision(mAP@0.5)improvements of 5.3%,8.8%,and 8.2% compared to existing models such as Faster R-CNN,SSD,and YOLOv7.Compared to the baseline YOLOv5 model,the mAP increased from 80.3% to 85.5%.The improved model demonstrates higher accuracy and robustness in handling complex backgrounds and overlapping crowns,and excels in adaptability across various scenarios,effectively supporting urban greening management and ecological monitoring.

Key words: individual tree detection, deep learning, unmanned aerial vehicle(UAV), ConvNeXt, YOLOv5