Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection

• URL: http://arxiv.org/abs/2104.03054v1
• Date: Wed, 7 Apr 2021 11:06:15 GMT
• Title: Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection
• Authors: Immanuel Weber, Jens Bongartz, Ribana Roscher
• Abstract summary: We propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance.
• Score: 1.4528189330418975
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used. A major challenge in such approaches is the limited amount of data that arises, for example, when more specialized and rarer vehicles such as agricultural machinery or construction vehicles are to be detected. This lack of data contrasts with the enormous data hunger of deep learning methods in general and object recognition in particular. In this article, we address this issue in the context of the detection of road vehicles in aerial images. To overcome the lack of annotated data, we propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance. In cases of very limited or even no real-world images, we observe an improvement in average precision of up to 0.70 points. We address the remaining performance gap to real-world datasets by analyzing the effect of the image composition of background and objects and give insights into the importance of background.

Related papers

• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• On the Robustness of Object Detection Models in Aerial Images [37.50307094643692]
  We introduce two novel benchmarks based on DOTA-v1.0. The first benchmark encompasses 19 prevalent corruptions, while the second focuses on cloud-corrupted images. We find that enhanced model architectures, larger networks, well-crafted modules, and judicious data augmentation strategies collectively enhance the robustness of aerial object detection models.
  arXiv  Detail & Related papers  (2023-08-29T15:16:51Z)
• CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components [77.33782775860028]
  We introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-07-24T11:59:07Z)
• LARD - Landing Approach Runway Detection -- Dataset for Vision Based Landing [2.7400353551392853]
  We present a dataset of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks.
  arXiv  Detail & Related papers  (2023-04-05T08:25:55Z)
• Aerial Image Object Detection With Vision Transformer Detector (ViTDet) [0.0]
  Vision Transformer Detector (ViTDet) was proposed to extract multi-scale features for object detection. ViTDet's simple design achieves good performance on natural scene images and can be easily embedded into any detector architecture. Our results show that ViTDet can consistently outperform its convolutional neural network counterparts on horizontal bounding box (HBB) object detection.
  arXiv  Detail & Related papers  (2023-01-28T02:25:30Z)
• Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery [0.0]
  State-of-the-art object detection methods largely fail to identify small and dense objects. We propose a small object detection pipeline that improves the feature extraction process. We show we can alleviate the degradation of object identification in previously unseen datasets.
  arXiv  Detail & Related papers  (2022-09-06T15:16:35Z)
• A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth [61.90504318229845]
  This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene. A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
  arXiv  Detail & Related papers  (2022-06-13T19:14:06Z)
• Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach [1.3359609092684614]
  In recent years, photogrammetry has been widely used in many areas to create 3D virtual data representing the physical environment. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations.
  arXiv  Detail & Related papers  (2021-09-24T22:29:26Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• Deep Traffic Sign Detection and Recognition Without Target Domain Real Images [52.079665469286496]
  We propose a novel database generation method that requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one.
  arXiv  Detail & Related papers  (2020-07-30T21:06:47Z)

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.