Adaptive Image Zoom-in with Bounding Box Transformation for UAV Object Detection

• URL: http://arxiv.org/abs/2602.07512v2
• Date: Thu, 12 Feb 2026 04:42:25 GMT
• Title: Adaptive Image Zoom-in with Bounding Box Transformation for UAV Object Detection
• Authors: Tao Wang, Chenyu Lin, Chenwei Tang, Jizhe Zhou, Deng Xiong, Jianan Li, Jian Zhao, Jiancheng Lv,
• Abstract summary: This work explores a simple and efficient adaptive zoom-in framework for object detection on UAV images.<n>The main motivation is that the foreground objects are generally smaller and sparser than those in common scene images.<n>The proposed ZoomDet is architecture-independent and can be applied to an arbitrary object detection architecture.
• Score: 36.46244505181511
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 https://github.com/twangnh/zoomdet_code.

Related papers

• Visible and Clear: Finding Tiny Objects in Difference Map [50.54061010335082]
  We introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. We further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear.
  arXiv  Detail & Related papers  (2024-05-18T12:22:26Z)
• YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images [33.80392696735718]
  YOLC (You Only Look Clusters) is an efficient and effective framework that builds on an anchor-free object detector, CenterNet. To overcome the challenges posed by large-scale images and non-uniform object distribution, we introduce a Local Scale Module (LSM) that adaptively searches cluster regions for zooming in for accurate detection. We perform extensive experiments on two aerial image datasets, including Visdrone 2019 and UAVDT, to demonstrate the effectiveness and superiority of our proposed approach.
  arXiv  Detail & Related papers  (2024-04-09T10:03:44Z)
• FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow [27.621524657473945]
  Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object appearance, and very large and/or irregular motion. We propose FOLT to mitigate these problems and reach fast and accurate MOT in UAV view.
  arXiv  Detail & Related papers  (2023-08-14T15:24:44Z)
• Learning to Zoom and Unzoom [49.587516562644836]
  We "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. We demonstrate this versatility by evaluating on a variety of tasks and datasets.
  arXiv  Detail & Related papers  (2023-03-27T17:03:30Z)
• Adaptive Rotated Convolution for Rotated Object Detection [96.94590550217718]
  We present Adaptive Rotated Convolution (ARC) module to handle rotated object detection problem. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images. The proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP.
  arXiv  Detail & Related papers  (2023-03-14T11:53:12Z)
• OA-DET3D: Embedding Object Awareness as a General Plug-in for Multi-Camera 3D Object Detection [77.43427778037203]
  We introduce OA-DET3D, a plug-in module that improves 3D object detection.<n> OA-DET3D boosts the representation of objects by leveraging object-centric depth information and foreground pseudo points.<n>We conduct extensive experiments on the nuScenes dataset and Argoverse 2 dataset to validate the merits of OA-DET3D.
  arXiv  Detail & Related papers  (2023-01-13T06:02:31Z)
• You Better Look Twice: a new perspective for designing accurate detectors with reduced computations [56.34005280792013]
  BLT-net is a new low-computation two-stage object detection architecture. It reduces computations by separating objects from background using a very lite first-stage. Resulting image proposals are then processed in the second-stage by a highly accurate model.
  arXiv  Detail & Related papers  (2021-07-21T12:39:51Z)
• ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection [69.68263074432224]
  We present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in RGB images for more accurate disparity estimation, we introduce a conceptually straight-forward module -- adaptive zooming.
  arXiv  Detail & Related papers  (2020-03-01T17:18:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.