Tree Species Recognition Method from UAV Remote Sensing Images Based on Improved YOLOv8
In order to identify different tree species by combining information of surroundings,high-resolution Unmanned Aerial Vehicle(UAV)image datasets of Pinus spp.,Cunninghamia lanceolata,Eucalyptus spp.and other broad-leaved trees were established to verify recognition effect of YOLOv8-LSK algorithm.Generaliza-tion ability of YOLOv8-LSK algorithm was verified by public dataset.To verify accuracy rate of YOLOv8-LSK algorithm,YOLOv8-LSK algorithm was compared with 5 algorithms.Effectiveness of YOLOv8-LSK algorithm was verified by ablation experiment.To verify spatial attention effect,YOLOv8 algorithm was used as baseline,and LSK module was compared with different light weight modules.Results showed that compared with R3Det,CFA,AOPG and RVSA algorithms,YOLOv8-LSK algorithm had higher generalization ability,with the highest mAP of 81.23%.Compared with TridentNet,RT-DETR,ReDet,Faster-RCNN and RTMDet algorithms,YO-LOv8-LSK algorithm had higher accuracy rate,with the highest mAP of 78.61%.Ablation experiment results showed that YOLOv8-LSK algorithm had significantly higher mAP compared with YOLOv8,YOLOv7 and YO-LOv6 algorithms.Compared with CBAM,SKNet and ConvNext modules,YOLOv8-LSK algorithm had the high-est mAP(78.61%).Boundaries of tree patches identified by YOLOv8-LSK algorithm were more obvious and re-ceptive fields were larger.
deep learningpatch zoningtree species recognitionforest resource surveyYOLOv8
万方数据
维普
