CA-YOLOX: Deep Learning-Guided Road Intersection Location From High-Resolution Remote Sensing Images

Abstract

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.