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A Joint Cross-Modal Super-Resolution Approach For Vehicle Detection in Aerial Imagery

机译：航空影像中车辆检测的一种联合跨模态超分辨率方法

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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.
机译：尽管在计算机视觉研究领域已经取得了一些突破，但航空影像中的车辆检测仍然是一个开放的研究挑战。大多数现有的最先进的车辆检测算法都忽略了考虑可能对检测任务产生重大影响的一些主要因素。航空影像的低分辨率特性被认为是主要因素之一。尽管超分辨率技术可以解决该问题，它学习了低分辨率（LR）图像与其对应的高分辨率（HR）对应图像之间的映射，但是，当需要在夜间或夜间进行检测时，该问题仍然存在。黑暗的环境。因此，基于RGB的检测可能是专门针对黑暗环境中检测任务的另一个重要问题。对于这样的环境，红外（IR）成像变得很有必要，在训练IR检测器的过程中再次可能无法使用。为了解决这些挑战，我们提出了一个基于生成对抗网络（GAN）的联合跨模式和超分辨率框架，用于航空图像中的车辆检测。我们建议的联合网络由两个深层子网组成。第一个子网利用GAN架构生成跨两个不同域的超分辨（SR）图像（跨域转换）。第二子网使用最先进的对象检测器之一，即“您只看一次”版本3（YOLOv3），对这些跨域转换和超分辨率的图像执行检测。为了评估我们提出的模型的有效性，我们对公众可获得的航空影像中的车辆检测（VEDAI）数据集进行了一些实验。我们进一步将我们提出的网络与最新的图像生成方法进行比较，以证明我们的模型是足够的。

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