基于霍夫变换和YOLOv3的车道和车辆检测

Subash Kumar, Kartikeya, S. Sushanth Kumar, Nikhil Gupta, Agrima Yadav
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引用次数: 1

摘要

在夜间跟踪目标对于减少夜间交通事故的数量至关重要。本文提出了一种称为M-YOLO的深度卷积神经网络,以提高夜间物体识别的精度,并适用于有限的环境(也包括汽车中的微控制器)。首先,根据轨道线图像时空色散密度不均匀的特点,将轨道线图像分成其他* 2S面板。此外,传感器频率被限制在四个测量级别,使其更适合微小的源定位,如横向距离测量。第三,为了优化连通性,在基本的Yolo v3方法中,全连接层减少了53到49层。最后,增强了簇中心半径和反向传播等特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lane and Vehicle Detection Using Hough Transform and YOLOv3
Object tracking at dark is critical to minimizing the number of nocturnal traffic crashes. This paper presents a deep convolutional neural network dubbed M-YOLO to enhance the precision of nocturnal object recognition and to be suited for limited contexts (also including microcontrollers in automobiles). To begin, track line images are separated into other * 2S panels based on the features of uneven spatial and temporal dispersion densities. Additionally, the sensor frequency has been limited to four measurement levels, making it even more suited for tiny source localization, like lateral distance measurement. Thirdly, to optimize the connectivity, a fully connected layer throughout the basic Yolo v3 method is reduced by 53 to 49 levels. Lastly, characteristics like cluster center radius and backpropagation are enhanced.
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