计算机科学
人工智能
棱锥(几何)
深度学习
卷积神经网络
交叉口(航空)
特征(语言学)
联营
光学(聚焦)
分割
计算机视觉
模式识别(心理学)
特征学习
特征提取
遥感
地理
地图学
数学
语言学
光学
物理
哲学
几何学
作者
Chengfan Li,Zixuan Zhang,Lan Liu,Shengnan Wang,Junjuan Zhao,Xuefeng Liu
标识
DOI:10.1142/s0218001423510175
摘要
The location of road intersection from high resolution remote sensing (HRRS) images can be automatically obtained by deep learning. This has become one of the current data sources in urban smart transportation. However, limited by the small size, diverse types, complex distribution, and missing sample labels of road intersections in actual scenarios, it is difficult to accurately represent key features of road intersection by deep neural network (DNN) model. A new coordinate attention (CA) module-YOLOX (CA-YOLOX) method for accurately locating road intersections from HRRS images is presented. First, the spatial pyramid pooling (SPP) module is introduced into the backbone convolution network between the Darknet-53’ last feature layer and feature pyramid networks (FPN) structure. Second, the CA module is embedded into the feature fusion structure in FPN to focus more on the spatial shape distribution and texture features of road intersections. Third, we use focal loss to replace the traditional binary cross entropy (BCE) loss in the confidence loss to improve the iteration speed of the CA-YOLOX network. Finally, an extensive empirical experiment on Potsdam, IKONOS datasets, and ablation study is then implemented and tested. The results show that the presented CA-YOLOX method can promote the location accuracy of road intersection from HRRS images compared to the traditional You only look once (YOLO) model.
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