Efficient resource management in UAVs for Visual Assistance

• URL: http://arxiv.org/abs/2007.05854v3
• Date: Tue, 4 Aug 2020 17:12:26 GMT
• Title: Efficient resource management in UAVs for Visual Assistance
• Authors: Bapireddy Karri
• Abstract summary: There is an increased interest in the use of Unmanned Aerial Vehicles (UAVs) for agriculture, military, disaster management and aerial photography. One of major challenges in using UAVs for Visual Assistance tasks in real time is managing the memory usage and power consumption. This project describes a novel method to optimize the general image processing tasks like object tracking and object detection for UAV hardware in real time scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is an increased interest in the use of Unmanned Aerial Vehicles (UAVs) for agriculture, military, disaster management and aerial photography around the world. UAVs are scalable, flexible and are useful in various environments where direct human intervention is difficult. In general, the use of UAVs with cameras mounted to them has increased in number due to their wide range of applications in real life scenarios. With the advent of deep learning models in computer vision many models have shown great success in visual tasks. But most of evaluation models are done on high end CPUs and GPUs. One of major challenges in using UAVs for Visual Assistance tasks in real time is managing the memory usage and power consumption of the these tasks which are computationally intensive and are difficult to be performed on low end processor board of the UAV. This projects describes a novel method to optimize the general image processing tasks like object tracking and object detection for UAV hardware in real time scenarios without affecting the flight time and not tampering the latency and accuracy of these models.

Related papers

• UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection [0.03464344220266879]
  Patch Intensity Convergence (PIC) technique generates high-fidelity bounding boxes for UAV detection without manual labeling. This technique forms the foundation of UAVDB, a dedicated database designed specifically for UAV detection. We benchmark UAVDB using state-of-the-art (SOTA) YOLO series detectors, providing a comprehensive performance analysis.
  arXiv  Detail & Related papers  (2024-09-09T13:27:53Z)
• Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System [56.51247807483176]
  Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military. Previous works have simplified such an anti-UAV task as a tracking problem, where prior information of UAVs is always provided. In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information.
  arXiv  Detail & Related papers  (2023-06-27T19:30:23Z)
• Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis [54.07535860237662]
  We propose a novel task for learned UAV video coding and construct a comprehensive and systematic benchmark for such a task. It is expected that the benchmark will accelerate the research and development in video coding on drone platforms.
  arXiv  Detail & Related papers  (2023-01-15T15:18:02Z)
• 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)
• SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water [4.625920569634467]
  Unmanned Aerial Vehicles (UAVs) are known for their fast and versatile applicability. We present a new benchmark suite, textittextbfSeaDroneSim, that can be used to create photo-realistic aerial image datasets. We obtain 71 mAP on real aerial images for detecting BlueROV as a feasibility study.
  arXiv  Detail & Related papers  (2022-10-26T21:50:50Z)
• Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks [3.6118662460334527]
  We introduce a Deep Q-Learning (DQL) approach to solve this multi-objective problem. We show that our proposed DQL-based method achieves comparable results when it comes to the UAVs' remaining battery levels and percentage of deadline violations.
  arXiv  Detail & Related papers  (2022-09-15T15:29:57Z)
• 3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning [75.78929539923749]
  Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. It is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT) In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices.
  arXiv  Detail & Related papers  (2021-06-06T14:08:41Z)
• EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature Fusion [8.634988828030245]
  This article focuses on the efficient aerial image classification from on-board a UAV for emergency response/monitoring applications. A dedicated Aerial Image Database for Emergency Response applications is introduced and a comparative analysis of existing approaches is performed. A lightweight convolutional neural network architecture is proposed, referred to as EmergencyNet, based on atrous convolutions to process multiresolution features.
  arXiv  Detail & Related papers  (2021-04-28T20:24:10Z)
• UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification [21.48667873335246]
  Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. The coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras. We propose the first new UAV re-identification data set, UAV-reID, that facilitates the development of machine learning solutions in this emerging area.
  arXiv  Detail & Related papers  (2021-04-13T14:13:09Z)
• Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking [59.06167734555191]
  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. We consider the task of tracking UAVs, providing rich information such as location and trajectory. We propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes.
  arXiv  Detail & Related papers  (2021-01-21T07:00:15Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

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.