Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems

• URL: http://arxiv.org/abs/2112.10948v1
• Date: Tue, 21 Dec 2021 02:44:49 GMT
• Title: Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems
• Authors: Xu Kang, Bin Song, Jie Guo, Zhijin Qin, F. Richard Yu
• Abstract summary: We propose a new aerial image transmission paradigm for the scene classification task. A lightweight model is developed on the front-end UAV for semantic blocks transmission with perception of images and channel conditions. In order to achieve the tradeoff between transmission latency and classification accuracy, deep reinforcement learning is used.
• Score: 46.64800170644672
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The vigorous developments of Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL). Collecting a large amount of image/video data, Unmanned aerial vehicles (UAVs) can only handover intelligent analysis tasks to the back-end mobile edge computing (MEC) server due to their limited storage and computing capabilities. How to efficiently transmit the most correlated information for the AI model is a challenging topic. Inspired by the task-oriented communication in recent years, we propose a new aerial image transmission paradigm for the scene classification task. A lightweight model is developed on the front-end UAV for semantic blocks transmission with perception of images and channel conditions. In order to achieve the tradeoff between transmission latency and classification accuracy, deep reinforcement learning (DRL) is used to explore the semantic blocks which have the best contribution to the back-end classifier under various channel conditions. Experimental results show that the proposed method can significantly improve classification accuracy compared to the fixed transmission strategy and traditional content perception methods.

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