Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks

• URL: http://arxiv.org/abs/2107.04648v1
• Date: Fri, 9 Jul 2021 19:47:02 GMT
• Title: Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks
• Authors: Marwan Dhuheir, Emna Baccour, Aiman Erbad, Sinan Sabeeh, Mounir Hamdi
• Abstract summary: We present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. We introduce an online solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs.
• Score: 9.449650062296824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention due to their outstanding ability to be used in different sectors and serve in difficult and dangerous areas. Moreover, the advancements in computer vision and artificial intelligence have increased the use of UAVs in various applications and solutions, such as forest fires detection and borders monitoring. However, using deep neural networks (DNNs) with UAVs introduces several challenges of processing deeper networks and complex models, which restricts their on-board computation. In this work, we present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board and finds the minimum decision-making latency. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. The formulated optimization solution is an NP-hard problem. Hence it is not adequate for online resource allocation. Therefore, we introduce an online heuristic solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs. The proposed approach is general enough to be used for different low decision-latency applications as well as for all CNN types organized into the pipeline of layers (e.g., VGG) or based on residual blocks (e.g., ResNet).

Related papers

• GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network [14.857267338331708]
  This paper proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users.
  arXiv  Detail & Related papers  (2024-08-18T02:29:10Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms [6.649753747542209]
  This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm. The formulated problem is NP-hard so finding the optimal solution is quite complex. We conduct extensive simulations and compare our results to the-state-of-the-art studies demonstrating that our model outperforms the competing models.
  arXiv  Detail & Related papers  (2022-12-21T17:16:42Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach [56.84948632954274]
  This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network. We propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network.
  arXiv  Detail & Related papers  (2021-12-20T15:45:28Z)
• Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization [43.9909417652678]
  Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones. Thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board.
  arXiv  Detail & Related papers  (2021-05-23T20:19:43Z)
• 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)
• Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning [1.2330326247154968]
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs. We propose an offline temporal difference (TD) learning algorithm with online signal-to-interference-plus-noise ratio mapping to solve the problem.
  arXiv  Detail & Related papers  (2021-04-09T16:52:33Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)

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.