UAVs and Neural Networks for search and rescue missions

• URL: http://arxiv.org/abs/2310.05512v1
• Date: Mon, 9 Oct 2023 08:27:35 GMT
• Title: UAVs and Neural Networks for search and rescue missions
• Authors: Hartmut Surmann and Artur Leinweber and Gerhard Senkowski and Julien Meine and Dominik Slomma
• Abstract summary: We present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) To achieve this, we use artificial neural networks and create a dataset for supervised learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with automatically labeled images. Finally, we evaluate the performance of different neural networks.

Related papers

• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• An Empirical Study of Remote Sensing Pretraining [117.90699699469639]
  We conduct an empirical study of remote sensing pretraining (RSP) on aerial images. RSP can help deliver distinctive performances in scene recognition tasks. RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, but it may still suffer from task discrepancies.
  arXiv  Detail & Related papers  (2022-04-06T13:38:11Z)
• Graph-based Active Learning for Semi-supervised Classification of SAR Data [8.92985438874948]
  We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods. CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting. The method easily incorporates a human-in-the-loop for active learning in the data-labeling process.
  arXiv  Detail & Related papers  (2022-03-31T00:14:06Z)
• Toward Data-Driven STAP Radar [23.333816677794115]
  We characterize our data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich example dataset of received radar signals by randomly placing targets of variable strengths in a predetermined region. For each data sample within this region, we generate heatmap tensors in range, azimuth, and elevation of the output power of a beamformer. In an airborne scenario, the moving radar creates a sequence of these time-indexed image stacks, resembling a video.
  arXiv  Detail & Related papers  (2022-01-26T02:28:13Z)
• Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
  Deep image prior was introduced as an effective prior for image reconstruction. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. We develop a two-stage learning paradigm to address the computational challenge.
  arXiv  Detail & Related papers  (2021-11-23T15:08:26Z)
• Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
  We propose a self-supervised approach for pre-training deep neural networks in remote sensing. By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner. We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
  arXiv  Detail & Related papers  (2021-08-02T07:50:50Z)
• Data Augmentation for Object Detection via Differentiable Neural Rendering [71.00447761415388]
  It is challenging to train a robust object detector when annotated data is scarce. Existing approaches to tackle this problem include semi-supervised learning that interpolates labeled data from unlabeled data. We introduce an offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
  arXiv  Detail & Related papers  (2021-03-04T06:31:06Z)
• Improving Object Detection in Art Images Using Only Style Transfer [5.156484100374058]
  We propose and evaluate a process for training neural networks to localize objects - specifically people - in art images. We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer. The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.
  arXiv  Detail & Related papers  (2021-02-12T13:48:46Z)
• UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance [20.318367304051176]
  We propose a holistic anomaly detection system using deep neural networks for surveillance of critical infrastructures. First, we present a method for the explicit representation of spatial layouts of objects in bird-view images. Then, we propose a deep neural network architecture for unsupervised anomaly detection (UAV-AdNet) Unlike studies in the literature, we combine GPS and image data to predict abnormal observations.
  arXiv  Detail & Related papers  (2020-11-05T14:26:29Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)
• RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar Image Formation [0.0]
  We train a deep neural network that performs both the image formation and image processing tasks, integrating the SAR processing pipeline. Results show that our integrated pipeline can output accurately classified SAR imagery with image quality comparable to those formed using a traditional algorithm.
  arXiv  Detail & Related papers  (2020-01-22T18:44:40Z)

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.