Fully Convolutional Networks for Street Furniture Identification in Panorama Images  

作　　者：Ying AO Penglong LI Li WEN Tao ZHANG Yanwen WANG 

机构地区：[1]Chongqing Geomatics and Remote Sensing Center,Chongqing 401147,China [2]Faculty of Geo-Information Science and Earth Observation(ITC),University of Twente,Enschede 7514AE,the Netherlands

出　　处：《Journal of Geodesy and Geoinformation Science》2022年第4期59-71,共13页测绘学报（英文版）

基　　金：Chongqing Natural Science(No.cstc2021jcyj-msxmX1203)

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.

关 键 词：panoramic images semantic segmentation street furniture object identification fully convolutional networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] U463.6[机械工程—车辆工程]