基于深度学习的无人机遥感影像车辆检测

Vehicle Detection from UAV Remote Sensing Images Based on Deep Learning

伴随着城市的发展,车辆数量在不断地增加。这一现象不仅增加了城市拥挤状态,而且还促使交通事故频发。要提高城市治理能力,就必须提高对城市车辆的监测能力。使用无人机对上海、赤峰地区的四个场景进行了低空摄影,获取了航空遥感影像数据,然后结合深度学习的Unet卷积神经网络技术对无人机影像中的车辆进行了单目标提取。结果表明,深度学习对无人机影像中车辆的识别能力远高于传统机器学习中的随机森林方法,达到了99%的超高精确度,且每个场景内汽车数的估算结果与真实数量极其接近。根据研究结果可知,将无人机和深度学习技术相结合的车辆检测方法具备实时性和现实可行性,可为城市的车辆实时监测和交通管理提供可靠的技术手段。

With the development of the city,the number of vehicles is increasing.This phenomenon not only increases urban congestion,but also leads to frequent traffic accidents.In order to improve the ability of urban governance,it is necessary to improve the monitoring ability of urban vehicles.In this paper,UAV is used to take low-altitude photography of four scenes in Shanghai and Chifeng area,and aerial remote sensing image data are obtained.Then,single target extraction is carried out for vehicles in UAV images combined with Unet convolutional neural network technology of deep learning.The results show that the ability of deep learning to recognize vehicles in UAV images is much higher than the random forest method in traditional machine learning,which reaches an ultra-high accuracy of 99%.And the estimation result of the number of cars in each scene is very close to the real number.According to the research results,the vehicle detection method combining UAV and deep learning technology has real time and practical feasibility,which can provide reliable technical means for real-time vehicle monitoring and traffic management in cities.