Abstract
Detecting objects from UAV-captured images is challenging due to the small object size. In this work, a simple and efficient adaptive zoom-in framework is explored for object detection on UAV images. The main motivation is that the foreground objects are generally smaller and sparser than those in common scene images, which hinders the optimization of effective object detectors. We thus aim to zoom in adaptively on the objects to better capture object features for the detection task. To achieve the goal, two core designs are required: \textcolor{black}{i) How to conduct non-uniform zooming on each image efficiently? ii) How to enable object detection training and inference with the zoomed image space?} Correspondingly, a lightweight offset prediction scheme coupled with a novel box-based zooming objective is introduced to learn non-uniform zooming on the input image. Based on the learned zooming transformation, a corner-aligned bounding box transformation method is proposed. The method warps the ground-truth bounding boxes to the zoomed space to learn object detection, and warps the predicted bounding boxes back to the original space during inference. We conduct extensive experiments on three representative UAV object detection datasets, including VisDrone, UAVDT, and SeaDronesSee. The proposed ZoomDet is architecture-independent and can be applied to an arbitrary object detection architecture. Remarkably, on the SeaDronesSee dataset, ZoomDet offers more than 8.4 absolute gain of mAP with a Faster R-CNN model, with only about 3 ms additional latency. The code is available at this https URL.
Abstract (translated)
从无人机捕获的图像中检测对象由于目标尺寸较小而具有挑战性。在这项工作中，我们探索了一种简单且高效的自适应缩放框架，用于无人机图像中的目标检测。主要动机是前景物体通常比常见场景图片中的物体更小、更稀疏，这阻碍了有效目标检测器的优化。因此，我们的目标是对每个对象进行自适应地放大，以便更好地捕捉用于检测任务的对象特征。 为了实现这一目标，需要两个核心设计： 1) 如何高效地对每张图像执行非均匀缩放？ 2) 如何使基于已缩放图像空间的目标检测训练和推理成为可能？ 为此，我们引入了一种轻量级偏移预测方案，并结合一种新颖的基于框的缩放目标，以学习在输入图象上的非均匀缩放。根据所学的缩放变换，提出了一种对角线对齐的边界框转换方法。该方法将地面实况边界框变形到已缩放的空间中进行目标检测训练，并在推断时将预测的边界框变形回原始空间。 我们在三个具有代表性的无人机目标检测数据集上进行了广泛的实验，包括VisDrone、UAVDT和SeaDronesSee。提出的ZoomDet是架构独立的，可以应用于任意对象检测架构。值得注意的是，在SeaDronesSee数据集上，使用Faster R-CNN模型时，ZoomDet比原方案提供了超过8.4的mAP绝对增益，并且仅增加了约3毫秒的额外延迟。 相关代码可在提供的URL处访问。
URL
https://arxiv.org/abs/2602.07512
