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Abstract <p>The development of science and technology has promoted the unmanned aerial vehicle (UAV) industry. Due to its small size, lightweight, low cost, and other characteristics, UAV can integrate with multiple industries, and promote social development, which broadens the use of UAV itself. UAVs have been widely used in aerial photography, agriculture, and disaster rescue. This paper analyzed the application of UAV in geological disaster rescue. Using UAV remote sensing to photograph the roads to the geological disaster area, the road conditions of different roads could be analyzed, providing the best rescue route in the disaster area. The current point-feature-based methods fail to accurately identify and analyze the target in the UAV image. This paper proposed a convolution neural network (CNN) based model to analyze the UAV image by automatically identifying the image targets. We investigated the accuracy of vehicle recognition using traditional UAV image recognition and our CNN-based model. The experimental results showed that the proposed method improved the average recognition accuracy by 9.35 and 9.08% in the road congestion environment and smooth roads, respectively, demonstrating the effectiveness of our proposed CNN-based method for intelligent recognition of UAV images.</p>

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Unmanned Aerial Vehicle Image Intelligent Recognition System Based on Machine Learning Algorithm

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Songjian Dan

摘要

Abstract

The development of science and technology has promoted the unmanned aerial vehicle (UAV) industry. Due to its small size, lightweight, low cost, and other characteristics, UAV can integrate with multiple industries, and promote social development, which broadens the use of UAV itself. UAVs have been widely used in aerial photography, agriculture, and disaster rescue. This paper analyzed the application of UAV in geological disaster rescue. Using UAV remote sensing to photograph the roads to the geological disaster area, the road conditions of different roads could be analyzed, providing the best rescue route in the disaster area. The current point-feature-based methods fail to accurately identify and analyze the target in the UAV image. This paper proposed a convolution neural network (CNN) based model to analyze the UAV image by automatically identifying the image targets. We investigated the accuracy of vehicle recognition using traditional UAV image recognition and our CNN-based model. The experimental results showed that the proposed method improved the average recognition accuracy by 9.35 and 9.08% in the road congestion environment and smooth roads, respectively, demonstrating the effectiveness of our proposed CNN-based method for intelligent recognition of UAV images.