A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog

• URL: http://arxiv.org/abs/2607.05467v1
• Date: Mon, 06 Jul 2026 07:06:37 GMT
• Title: A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
• Abstract summary: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery.<n>This paper presents a task-driven evaluation framework that links depth-aware synthetic fog generation, image restoration, and tracking.
• Score: 4.675397149564289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream detection and tracking. This paper presents a task-driven evaluation framework that links depth-aware synthetic fog generation, image restoration, object detection, and tracking within a unified pipeline. Given the practical difficulty of collecting and annotating foggy UAV scenes, synthetic fog is generated from real clear-weather outdoor images containing UAV targets using monocular depth estimation and the atmospheric scattering model. Representative restoration methods from classical, convolutional neural network (CNN)-based, and transformer-based families are first compared, after which the selected restoration model is integrated into the downstream perception pipeline. Detection is evaluated under both clean-only and fog-inclusive training regimes using multiple detector variants, while tracking-by-detection is assessed on clean, foggy, and restored video sequences. Beyond image-level restoration metrics, the study evaluates how fog and restoration affect detection robustness and tracking performance. The results show that fog substantially degrades both detection and tracking, primarily through increased missed detections. Fog-inclusive training provides the most consistent improvement in robustness, whereas test-time restoration is most beneficial when the detector has been trained only on clean imagery. These findings show that restoration quality does not necessarily translate into proportional gains in downstream perception and therefore should be evaluated jointly with detection and tracking performance.

Related papers

• TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors [76.4118981404995]
  Trajectory-Injected Generative Attack (TIGA) is a source-image-free and training free framework that generates detector-evasive images.<n>TIGA steers the latent Denoising Diffusion Implicit Model (DDIM) trajectory so that adversarial properties emerge during generation.<n>TIGA achieves strong blackbox attack performance, transferability, and high robustness under common post-processing operations.
  arXiv  Detail & Related papers  (2026-07-28T15:51:39Z)
• UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection [71.83097731030254]
  We propose a Unified Vision-to-Detection Network (UniV2D) to jointly optimize visual restoration and salient object detection.<n>UniV2D introduces a semantic-driven learning paradigm: high-level saliency semantics actively guide the restoration process.<n>It significantly outperforms state-of-the-art methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2026-05-08T02:31:45Z)
• Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification [54.952203312050564]
  Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification.<n>We propose FogFool, a physically plausible adversarial framework that generates fog-based perturbations.<n>Experiments on two benchmark RS datasets demonstrate that FogFool achieves superior performance.
  arXiv  Detail & Related papers  (2026-04-16T05:47:57Z)
• Blur-Robust Detection via Feature Restoration: An End-to-End Framework for Prior-Guided Infrared UAV Target Detection [29.207964743160968]
  Infrared unmanned aerial vehicle (UAV) target images often suffer from motion blur degradation.<n>We propose a novel Joint Feature-Domain Deblurring and Detection end-to-end framework, dubbed JFD3.<n>Experiments on IRBlurUAV demonstrate that JFD3 achieves superior detection performance while maintaining real-time efficiency.
  arXiv  Detail & Related papers  (2025-11-18T11:27:15Z)
• From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance [2.0524609401792397]
  We present a structured empirical study that benchmarks a comprehensive set of pipelines.<n>We assess both image quality and downstream performance on object detection (mAP) and segmentation (PQ, RQ, SQ)<n>Our analysis reveals when defogging helps, when chaining yields synergy or degradation, and how VLM-based editors compare to dedicated approaches.
  arXiv  Detail & Related papers  (2025-10-04T19:05:04Z)
• Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAVTarget Detection [18.776245480405958]
  Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature dependent lowfrequency nonuniformity.<n>We present a detection-friendly union framework that simultaneously addresses both infrared and UAV target detection tasks.<n>We introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels.
  arXiv  Detail & Related papers  (2025-04-05T01:29:22Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• D-YOLO a robust framework for object detection in adverse weather conditions [0.0]
  Adverse weather conditions including haze, snow and rain lead to decline in image qualities, which often causes a decline in performance for deep-learning based detection networks. To better integrate image restoration and object detection tasks, we designed a double-route network with an attention feature fusion module. We also proposed a subnetwork to provide haze-free features to the detection network. Specifically, our D-YOLO improves the performance of the detection network by minimizing the distance between the clear feature extraction subnetwork and detection network.
  arXiv  Detail & Related papers  (2024-03-14T09:57:15Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning [51.07496081296863]
  We recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy. We propose a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers. We build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks.
  arXiv  Detail & Related papers  (2022-05-16T04:24:22Z)
• RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems [8.430502131775722]
  Weather corruptions can hinder the object detectability and pose a serious threat to their navigation and reliability. We propose a contrastive approach towards mitigating this problem, by evaluating images generated by restoration models during and post training. Our approach achieves an averaged 178 percent increase in mAP between the input and restored images under adverse weather conditions.
  arXiv  Detail & Related papers  (2022-04-03T12:45:00Z)

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.