Pedestrian and Vehicles Detection with ResNet in Aerial Images

Enes Cengiz, C. Yilmaz, H. Kahraman, F. Bayram
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引用次数: 5

Abstract

In today's applications, a significant increase in the use of deep learning algorithms is noticeable. The convolution neural network (CNN) of deep learning has been used frequently recently, especially for the successful discrimination of people and vehicles from other objects. Especially with the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, the use of CNNs has become widespread. With the development of technology and traditional image processing techniques, the proses of image processing has been considerably reduced, furthermore, the success rate has increased dramatically. Object detection can be difficult due to the low resolution of objects in aerial images. In this study, a system which automatically recognizes human and different types of vehicles (cars, bicycles, motorcycles) from aerial images taken with drone has been developed. In the system, Residual Networks (ResNet) model, which is the first in the ImageNet competition of the CNN been one of the deep learning techniques, is used. Google Colaboratory with Nvidia Tesla K80 GPU support is used for successful and fast training and testing of the system. In the developed system, results are explained according to different threshold values of the objects detected from the images applied to the input.
基于ResNet的航拍图像行人和车辆检测
在今天的应用中,深度学习算法的使用显著增加是显而易见的。近年来,深度学习的卷积神经网络(CNN)得到了广泛的应用,特别是在人与车辆与其他物体的区分中取得了成功。特别是随着2012年ImageNet大规模视觉识别挑战赛(ILSVRC)的到来,cnn的应用变得越来越广泛。随着技术和传统图像处理技术的发展,图像处理的过程大大减少,成功率大大提高。由于航拍图像中物体的低分辨率,目标检测可能会很困难。在本研究中,开发了一种从无人机拍摄的航拍图像中自动识别人和不同类型车辆(汽车、自行车、摩托车)的系统。该系统采用了CNN ImageNet竞赛中首次采用的深度学习技术之一的残余网络(ResNet)模型。支持Nvidia Tesla K80 GPU的Google协作实验室用于系统的成功和快速培训和测试。在开发的系统中,根据从应用于输入的图像中检测到的物体的不同阈值来解释结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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