Using GMOSTNet for Tree Detection Under Complex Illumination and Morphological Occlusion

查看全文 作　　者：Zheng [1,2]Qian;Hailin [1,3]Feng;Yinhui [1,2]Yang;Xiaochen [1,3]Du;Kai [1,3]Xia 机构地区：[1]School of Information Engineering,Zhejiang A&F University,Hangzhou,China;[2]Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province,Hangzhou,China;[3]Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment,Hangzhou,China 出　　处：《国际计算机前沿大会会议论文集》索引2020年第1期,共18页 基　　金：National Natural Science Foundation of China(U1809208). 摘　　要：Trees are an integral part of the forestry ecosystem.In forestry work,the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology.In order to minimize a series of inestimable problems,such as yield reduction,ecological damage,and destruction,caused by inaccurate acquisition of tree location information,this paper proposes a ground tree detection method GMOSTNet.Based on the four types of tree species in the GMOST dataset and Faster R-CNN,it extracted the features of the trees,generate candidate regions,classification,and other operations.By reducing the influence of illumination and occlusion factors during experimentation,more detailed information of the input image was obtained.Meanwhile,regarding false detections caused by inappropriate approximations,the deviation and proximity of the proposal were adjusted.The experimental results showed that the AP value of the four tree species is improved after using GMOSTNet,and the overall accuracy increases from the original 87.25%to 93.25%. 关 键 词：Tree detection ILLUMINATION OCCLUSION Deep learning