Target-Guided Feature Super-Resolution for Vehicle Detection in Remote Sensing Images
Jianxiang Li, Zili Zhang, Yan Tian, Yiping Xu, Yihong Wen, Shicheng Wang
Abstract
Vehicle detection in remote sensing images remains a challenge because most vehicles are small and cover only a relatively small area due to the low ground sample distance. Although image super-resolution can improve small object detection performance as a preprocessing step, methods for improving the quality of the entire image tend to focus on the majority backgrounds that are not important for detection and involve high computational cost. Inspired by the promising feature-level super-resolution method, in this letter, we propose a novel anchor-free vehicle detection network for small vehicle detection in remote sensing images. Specifically, a target-guided feature super-resolution network is proposed to enhance the features of the potential target. Besides, we propose a novel feature fusion module to improve the feature representation of shallow layers, which accounts for small object detection. Extensive experiments on three public remote sensing detection datasets [cars overhead with context (COWC), Vehicle Detection in Aerial Imagery (VEDAI), and UCAS-AOD] amply demonstrate that our method can achieve significant performance with a mean average precision of 0.933, 0.756, and 0.961, respectively.