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Vehicle detection in aerial imagery is still an open research challenge although it has received some breakthroughs in the computer vision research community. Most of the existing state-of-the-art vehicle detection algorithms have ignored to consider some major factors which may have a great influence on the detection task. The low-resolution characteristic of aerial images is considered one of the major factors. Although the super-resolution technique can resolve this problem which learns a mapping between the low-resolution (LR) images and their corresponding high-resolution (HR) counterparts, however, the problem still remains when detection needs to take place at night or in a dark environment. Therefore, RGB-based detection can be another vital problem specifically for the detection task in a dark environment. For such environment infrared (IR) imaging becomes necessary which again may not be available during training an IR detector. To address these challenges, we propose a joint cross-modal and super-resolution framework based on the Generative Adversarial Network (GAN) for vehicle detection in aerial images. Our proposed joint network consists of two deep sub-networks. The first sub-network utilizes the GAN architecture to generate super-resolved (SR) images across two different domains (cross-domain translation). The second sub-network performs detection on these cross-domain translated and super-resolved images using one of the state-of-the-art object detectors i.e., You Only Look Once version 3 (YOLOv3). To evaluate the efficacy of our proposed model, we conduct several experiments on a publicly available Vehicle Detection in Aerial Imagery (VEDAI) dataset. We further compare our proposed network with state-of-the-art image generation methods to show the adequacy of our model.
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