Mapping Road Safety Barriers Across Street View Image Sequences: A Hybrid Object Detection and Recurrent Model

Md. Mostafijur Rahman, Arpan Man Sainju, Dan Yan, Zhe Jiang
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引用次数: 2

Abstract

Road safety barriers (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety barriers are critical components of safety infrastructure management systems at federal or state transportation agencies. In current practice, mapping road safety barriers is largely done manually (e.g., driving on the road or visual interpretation of street view imagery), which is slow, tedious, and expensive. We propose a deep learning approach to automatically map road safety barriers from street view imagery. Our approach considers road barriers as long objects spanning across consecutive street view images in a sequence and use a hybrid object-detection and recurrent-network model. Preliminary results on real-world street view imagery show that the proposed model outperforms several baseline methods.
在街景图像序列中绘制道路安全屏障:一种混合目标检测和循环模型
道路安全屏障(如混凝土屏障、金属防撞屏障、防撞带)在预防或减轻车辆碰撞方面发挥着重要作用。道路安全屏障的精确地图是联邦或州交通机构安全基础设施管理系统的重要组成部分。在目前的实践中,道路安全屏障的测绘主要是手动完成的(例如,在道路上驾驶或街景图像的视觉解释),这是缓慢、繁琐和昂贵的。我们提出了一种深度学习方法,从街景图像中自动绘制道路安全屏障。我们的方法将道路障碍物视为跨越连续街景图像的长物体,并使用混合物体检测和循环网络模型。在真实街景图像上的初步结果表明,该模型优于几种基线方法。
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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