雪山隧道四类车辆单发多箱探测器探测

Chun-Ming Tsai, T. Shou, J. Hsieh, Kuang-Hsuan Chen
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引用次数: 1

摘要

台湾有很多车辆,因此有很多交通问题。特别是在春节和节假日期间,宜兰至台北之间的雪山隧道(HST)总是交通堵塞。为了解决这一问题,智能交通系统(ITS)必不可少,而准确的车辆检测(VD)是智能交通系统的第一阶段。为了在高速隧道中检测车辆,提出了基于单弹多盒探测器(SSD)的三种训练方法,对隧道中四类车辆进行检测。实验结果表明,与使用大量训练数据集的预训练SSD模型相比,本文提出的三种训练方法仅使用1000个训练帧,就能检测和分类出更多的车辆。具体来说,通过我们收集的数据集和数据增强训练的SSD对轿车、面包车、公共汽车和卡车的检测率最高,分别为93.6%、90.9%、100%和100%。
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Four Categories Vehicle Detection in Hsuehshan Tunnel via Single Shot Multibox Detector
Taiwan has many vehicles and as a result many traffic problems. In particular, during the Spring Festival and the holidays, Hsuehshan Tunnel (HST) between Yilan and Taipei is always a traffic jam. To solve this problem, intelligent transportation system (ITS) is necessary, and accurate vehicle detection (VD) is the first stage for ITS. In order to detect vehicles in HST, three training methods based on single shot multibox detector (SSD) are presented to detect four categories of vehicle in the Tunnel. The experimental results demonstrated that the presented three training methods, which only used 1000 training frames, can detect and categorize more vehicles than the pre-trained SSD model which used a large training dataset. Specifically, the SSD trained by our collected data set and data augmentation has the highest detection rates for sedan, van, bus, and truck – 93.6%, 90.9%, 100%, and 100%, respectively.
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