Synthetic Data and Hierarchical Object Detection in Overhead Imagery

• URL: http://arxiv.org/abs/2102.00103v1
• Date: Fri, 29 Jan 2021 22:52:47 GMT
• Title: Synthetic Data and Hierarchical Object Detection in Overhead Imagery
• Authors: Nathan Clement, Alan Schoen, Arnold Boedihardjo, and Andrew Jenkins
• Abstract summary: We develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satellite imagery. To test the effectiveness of synthetic imagery, we employ it in the training of detection models and our two stage model, and evaluate the resulting models on real satellite images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of neural network models is often limited by the availability of big data sets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satellite imagery. In addition to extending synthetic data generation approaches, we propose a hierarchical detection approach to improve the utility of synthetic training samples. We consider existing techniques for producing synthetic imagery--3D models and neural style transfer--as well as introducing our own adversarially trained reskinning network, the GAN-Reskinner, to blend 3D models. Additionally, we test the value of synthetic data in a two-stage, hierarchical detection/classification model of our own construction. To test the effectiveness of synthetic imagery, we employ it in the training of detection models and our two stage model, and evaluate the resulting models on real satellite images. All modalities of synthetic data are tested extensively on practical, geospatial analysis problems. Our experiments show that synthetic data developed using our approach can often enhance detection performance, particularly when combined with some real training images. When the only source of data is synthetic, our GAN-Reskinner often boosts performance over conventionally rendered 3D models and in all cases the hierarchical model outperforms the baseline end-to-end detection architecture.

Related papers

• Improving Object Detection by Modifying Synthetic Data with Explainable AI [3.0519884745675485]
  We propose a novel conceptual approach to improve the performance of computer vision models trained on synthetic images. We use robust Explainable AI (XAI) techniques to guide the modification of 3D models used to generate these images. We show that synthetic data can improve detection of vehicles in orientations unseen in training by 4.6%.
  arXiv  Detail & Related papers  (2024-12-02T13:24:43Z)
• Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets [4.696575161583618]
  This study focuses on camera-based traffic sign recognition applications for advanced driver assistance systems and autonomous driving. The proposed augmentation pipeline of synthetic datasets includes novel augmentation processes such as structured shadows and gaussian specular highlights. Experiments showed that a synthetic image-based approach outperforms in most cases real image-based training when applied to cross-domain test datasets.
  arXiv  Detail & Related papers  (2024-10-30T07:11:41Z)
• Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting [3.5351922399745166]
  This research introduces a novel method that employs 3D Gaussian Splatting to generate synthetic surgical datasets. We developed a data recording system capable of acquiring images alongside tool and camera poses in a surgical scene. Using this pose data, we synthetically replicate the scene, thereby enabling direct comparisons of the synthetic image quality.
  arXiv  Detail & Related papers  (2024-07-20T11:20:07Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation [66.3814684757376]
  This work presents Zero123-6D, the first work to demonstrate the utility of Diffusion Model-based novel-view-synthesizers in enhancing RGB 6D pose estimation at category-level. The outlined method shows reduction in data requirements, removal of the necessity of depth information in zero-shot category-level 6D pose estimation task, and increased performance, quantitatively demonstrated through experiments on the CO3D dataset.
  arXiv  Detail & Related papers  (2024-03-21T10:38:18Z)
• Learning from Synthetic Data for Visual Grounding [55.21937116752679]
  We show that SynGround can improve the localization capabilities of off-the-shelf vision-and-language models. Data generated with SynGround improves the pointing game accuracy of a pretrained ALBEF and BLIP models by 4.81% and 17.11% absolute percentage points, respectively.
  arXiv  Detail & Related papers  (2024-03-20T17:59:43Z)
• UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception [62.71374902455154]
  We leverage recent advancements in neural rendering to improve static and dynamic novelview UAV-based image rendering. We demonstrate a considerable performance boost when a state-of-the-art detection model is optimized primarily on hybrid sets of real and synthetic data.
  arXiv  Detail & Related papers  (2023-10-25T00:20:37Z)
• Robust Category-Level 3D Pose Estimation from Synthetic Data [17.247607850702558]
  We introduce SyntheticP3D, a new synthetic dataset for object pose estimation generated from CAD models. We propose a novel approach (CC3D) for training neural mesh models that perform pose estimation via inverse rendering.
  arXiv  Detail & Related papers  (2023-05-25T14:56:03Z)
• Synthetic Image Data for Deep Learning [0.294944680995069]
  Realistic synthetic image data rendered from 3D models can be used to augment image sets and train image classification semantic segmentation models. We show how high quality physically-based rendering and domain randomization can efficiently create a large synthetic dataset based on production 3D CAD models of a real vehicle.
  arXiv  Detail & Related papers  (2022-12-12T20:28:13Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)

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.