Abstract. Vehicle detection in remote sensing image has been attracting remarkable attention over past years for its applications in traffic, security, military, and surveillance fields. Due to the stunning success of deep learning techniques in object detection community, we consider to utilize CNNs for vehicle detection task in remote sensing image. Specifically, we take advantage of deep residual network, multi-scale feature fusion, hard example mining and homography augmentation to realize vehicle detection, which almost integrates all the advanced techniques in deep learning community. Furthermore, we simultaneously address super-resolution (SR) and detection problems of low-resolution (LR) image in an end-to-end manner. In consideration of the absence of paired low-/highresolution data which are generally time-consuming and cumbersome to collect, we leverage generative adversarial network (GAN) for unsupervised SR. Detection loss is back-propagated to SR generator to boost detection performance. We conduct experiments on representative benchmark datasets and demonstrate that our model yields significant improvements over state-of-the-art methods in deep learning and remote sensing areas.
VEHICLE DETECTION IN REMOTE SENSING IMAGES USING DEEP NEURAL NETWORKS AND MULTI-TASK LEARNING
