UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery

• URL: http://arxiv.org/abs/2402.05773v3
• Date: Sat, 13 Apr 2024 03:56:11 GMT
• Title: UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery
• Authors: Wenhui Chang, Hongming Chen, Xin He, Xiang Chen, Liangduo Shen,
• Abstract summary: Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. We first construct a new benchmark dataset for removing raindrops from UAV images, called UAV-Rain1k.
• Score: 18.97379943624394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. Despite recent progress in image deraining methods and datasets, there is a lack of focus on raindrop removal from UAV aerial imagery due to the unique challenges posed by varying angles and rapid movement during drone flight. To fill the gap in this research, we first construct a new benchmark dataset for removing raindrops from UAV images, called UAV-Rain1k. In this letter, we provide a dataset generation pipeline, which includes modeling raindrop shapes using Blender, collecting background images from various UAV angles, random sampling of rain masks and etc. Based on the proposed benchmark, we further present a comprehensive evaluation of existing representative image deraining algorithms, and reveal future research opportunities worth exploring. The proposed dataset is publicly available at https://github.com/cschenxiang/UAV-Rain1k.

Related papers

• Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines [17.3009572002435]
  Power Line Autonomous Inspection (PLAI) plays a crucial role in the construction of smart grids. PLAI is completed by accurately detecting the electrical components and defects in the aerial images captured byUAVs. The visible quality of aerial images is inevitably degraded by adverse weather like haze, rain, or snow, which are found to drastically decrease the detection accuracy in our research. We propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and high-quality images from degraded images with bad weather.
  arXiv  Detail & Related papers  (2024-09-07T12:53:05Z)
• Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception? [57.77643186237265]
  We present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial-view dataset, and the fourth largest among all drone-based datasets.
  arXiv  Detail & Related papers  (2023-12-07T18:59:14Z)
• Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts [20.20609511526255]
  Vision-based object detection methods have been developed for UAV detection. UAV detection in images with complex backgrounds and weather artifacts like rain has yet to be reasonably studied. This work also focuses on benchmarking state-of-the-art object detection models.
  arXiv  Detail & Related papers  (2023-05-25T19:54:33Z)
• ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering [83.75284107397003]
  We introduce ScatterNeRF, a neural rendering method which renders scenes and decomposes the fog-free background. We propose a disentangled representation for the scattering volume and the scene objects, and learn the scene reconstruction with physics-inspired losses. We validate our method by capturing multi-view In-the-Wild data and controlled captures in a large-scale fog chamber.
  arXiv  Detail & Related papers  (2023-05-03T13:24:06Z)
• UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios [0.6524460254566905]
  This paper constructs a multi-resolution UAV scenario dataset, called UAVStereo, with over 34k stereo image pairs covering 3 typical scenes. In this paper, we evaluate traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios.
  arXiv  Detail & Related papers  (2023-02-20T16:45:27Z)
• Toward Real-world Single Image Deraining: A New Benchmark and Beyond [79.5893880599847]
  Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs. We establish a new high-quality dataset named RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively.
  arXiv  Detail & Related papers  (2022-06-11T12:26:59Z)
• Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior [103.49307603952144]
  Most existing de-rain algorithms use only one single input image and aim to recover a clean image. We present a Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information. Experiments on both monocular and the newly proposed stereo rainy datasets demonstrate that the proposed method achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-09T04:15:10Z)
• Raindrops on Windshield: Dataset and Lightweight Gradient-Based Detection Algorithm [0.0]
  We present a new dataset for training and assessing vision algorithms' performance for different tasks of image artifacts detection on either camera lens or windshield. To augment the data, we also propose an algorithm for data augmentation which allows the generation of synthetic raindrops on images. The proposed algorithm showed a higher quality of raindrop presence detection and image processing speed, making it applicable for the self-check procedure of real autonomous systems.
  arXiv  Detail & Related papers  (2021-04-11T19:04:59Z)
• A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View [93.23947591795897]
  In this paper, we strive to tackle the challenges and automatically understand the crowd from the visual data collected from drones. To alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV)
  arXiv  Detail & Related papers  (2020-09-29T01:48:24Z)
• Multi-Scale Progressive Fusion Network for Single Image Deraining [84.0466298828417]
  Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features.
  arXiv  Detail & Related papers  (2020-03-24T17:22:37Z)
• AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance [20.318367304051176]
  Unmanned aerial vehicles (UAVs) with mounted cameras have the advantage of capturing aerial (bird-view) images. Several aerial datasets have been introduced, including visual data with object annotations. We propose a multi-purpose aerial dataset (AU-AIR) that has multi-modal sensor data collected in real-world outdoor environments.
  arXiv  Detail & Related papers  (2020-01-31T09:45:12Z)

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.