FM-SAM: individual tree crown delineation and classification based on Segmentation Anything Model (SAM) and YOLOv10 in UAV imagery for forest monitoring
Haohua Que, Haojia Gao, Weihao Shan, Mingkai Liu, Jianshuo An, Fei Deng, Shuo Feng, Xinghua Yang, Lei Mu
Abstract
Forest monitoring plays a crucial role in the sustainable management and conservation of natural resources, with tree crown delineation being a key task for assessing forest structure and tree health. This study presents a novel framework, FM-SAM, integrating YOLOv10 and the Segment Anything Model (SAM) for precise tree crown segmentation and species identification in UAV imagery. The framework combines YOLOv10’s real-time detection capabilities with SAM’s strong segmentation, significantly improving segmentation and classification accuracy. SAM is also employed as a semi-supervised branch in FM-SAM to expedite the annotation process, offering a solution that reduces the need for extensive manual labeling. Experimental results on the MixedForestDataset , comprising both coniferous and broadleaf tree species, demonstrate that FM-SAM outperforms traditional deep learning frameworks such as DeepLabv3 and YOLO-based models, achieving high accuracy, precision, and recall values. The proposed method excels in complex forest environments, enhancing the effectiveness of tree crown delineation and classification for large-scale forest monitoring applications. • FM-SAM framework merges YOLOv10 and SAM for high-accuracy tree crown segmentation. • Efficient labeling with Semi-Supervised Approach, making MixedForestDataset . • The brightness of the images is correlated with the segmentation accuracy.