基于卷积神经网络的无人机地标检测

Runfeng Yang, Xi Wang
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引用次数: 3

摘要

视觉感知技术在无人机上的广泛应用,给无人机在各个领域的应用带来了巨大的变化。对于无人机来说,地标图像的检测是一个挑战。无人机在不同环境下飞行时,由于路标方向的不确定性、路标类型的多样性和路标的相似性,导致了路标检测性能的严重恶化。提出了一种基于卷积神经网络(CNN)的无人机地标检测方法。理论分析和实验结果表明,该方法的地标识别精度达到96%以上,与部署在无人机上的地标识别相匹配,能够正确分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Landmark Detection Based on Convolutional Neural Network
The extensive use application of visual perception technology in Unmanned Aerial Vehicle (UAV) has brought great changes to the application of UAV in various fields. It is challenge to detect in landmark images for UAV. During UAV flight in different environments, the performance of landmark detection to deteriorate seriously have been caused by the uncertainty of landmark orientation, the diversity of landmark types and the similarities. This paper presents landmark detection of UAV based on Convolutional Neural Network (CNN). Theoretical analysis and experimental results demonstrate landmark recognition with an accuracy of at least 96% to match deployed in UAV, and the proposed CNN can make a correct classification.
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