典型文献
Fully Convolutional Networks for Street Furniture Identification in Panorama Images
文献摘要：
Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images.This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks(FCN).FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction.In this study,we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data.Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions.The results show that in all results from pre-trained model,fine-tuning,and FCN model with focal loss,the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.
文献关键词：
中图分类号：
作者姓名：
Ying AO;Penglong LI;Li WEN;Tao ZHANG;Yanwen WANG
作者机构：
Chongqing Geomatics and Remote Sensing Center,Chongqing 401147,China;Faculty of Geo-Information Science and Earth Observation(ITC),University of Twente,Enschede 7514AE,the Netherlands
文献出处：
引用格式：
[1]Ying AO;Penglong LI;Li WEN;Tao ZHANG;Yanwen WANG-.Fully Convolutional Networks for Street Furniture Identification in Panorama Images)[J].测绘学报（英文版）,2022(04):59-71
A类：
Furniture,Panorama,cityscape,finetune
B类：
Fully,Convolutional,Networks,Street,Identification,Images,Panoramic,images,are,widely,used,many,scenes,especially,virtual,reality,street,view,capture,However,they,new,furniture,identification,which,usually,mobile,laser,scanning,point,cloud,conventional,2D,This,study,proposes,semantic,segmentation,panoramic,transformed,separate,light,poles,traffic,signs,from,background,implemented,by,trained,FCN,most,important,model,deep,learning,applied,its,end,training,process,pixel,wise,In,this,8s,that,dataset,our,own,Then,replace,cross,entropy,loss,function,focal,again,produce,predictions,results,show,tuning,detected,well,have,better,performance,than,according,Recall,IoU,evaluation
AB值：
0.531831
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
