UAV Images of Pine Forests for Early Detection of Pine Wood Nematode Infestation
[Objecive]Aiming at the problem of missed and misdetection of targets due to the inconspicuous features and variable scales of early pine wood nematode infestation in UAV(unmanned aerial vehicle)forest images,this paper proposes a detection method for early pine wood nematode infestation.[Method]Firstly,in order to facilitate the deployment at the airborne end of the UAV,this paper proposes a pine wood nematode infestation detection method that reduces the amount of computation and the number of parameters;secondly,in order to obtain more accurate features of early pine wood nematode infestation,the method uses multiple ways of simultaneous extraction of features and fusion to enhance the learning ability of effective features;then,in order to further improve the fusion ability of features at different scales,the method uses the attention mechanism to Aligning the adjacent two levels of features;finally,taking the early pine nematode infestation photographed by UAV in Dahuofang experimental forest in Fushun,Liaoning Province as the object of study,the LablImg open source software was used to annotate the images captured at the heights of 100-240 m,and to construct the early pine nematode infestation images(EPI)(early pine wood nematode infestation)dataset.[Results]The test results on the EPI dataset showed that the Average Precision(AP)of this paper's method reached up to 95.2％,which was improved by 3.1％,4％and 0.9％compared with the AP of YOLOv5s,YOLOX-s and YOLOv6s,respectively;the volume of the model was only 12.8 M,which was the volume of YOLOv5s,YOLOx-s and YOLOv6s,respectively,23.6％,17.8％and 8.9％of the model volume of YOLOX-s and YOLOv6s,respectively.[Conclusion]In summary,the method in this paper has high recognition accuracy while keeping the model volume small,which provides the possibility for UAVs to recognize early pine wood nematode infestation at the airborne end.
万方数据
维普
