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

TL;DR

This paper introduces a task-oriented image transmission method for UAV-based scene classification, utilizing deep reinforcement learning to optimize semantic block transmission, significantly improving accuracy over traditional methods.

Contribution

It proposes a novel aerial image transmission paradigm using DRL to select semantic blocks, balancing latency and accuracy for UAV scene classification.

Findings

Significant accuracy improvement over fixed strategies.

Effective semantic block selection under varying channel conditions.

Enhanced UAV scene classification performance.

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.