Nur Nabilah Abu Mangshor, Norzihani Saharuddin, Shafaf Ibrahim, A. Fadzil, K. A. Samah
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引用次数: 3

摘要

在当今技术驱动的世界里,有很多设备和产品被发明出来,包括自动驾驶汽车(AV)。自动驾驶汽车是指能够自动驾驶的无人驾驶车辆。它在各种嵌入式系统和传感器的帮助下工作,帮助自动驾驶汽车发挥更大的作用。限速标志识别是自动驾驶汽车TSR的基本功能之一,它有助于自动检测和识别道路上的限速标志。道路上有许多限速标志,包括30公里/小时、60公里/小时、90公里/小时的标志等等。然而,这些限速标志之间的类间相似性给TSR系统在检测和识别过程中带来了挑战。因此,本研究提出了一种图像处理技术,利用单层网络(single Shot Multibox Detector, SSD)算法来开发TSR系统的限速标志识别。德国交通标志数据集(GTSD)用于训练模型,然后使用标准马来西亚限速标志的实时图像对模型进行测试。使用混淆矩阵进行精度测试,以确定系统的总体精度。在测试过程中,总共使用了100张图像,该系统对限速标志的检测和识别准确率达到了平均准确率的92.4%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Real-Time Speed Limit Sign Recognition System for Autonomous Vehicle Using SSD Algorithm
In today’s technology-driven world, there are a lot of devices and products have been invented including the Autonomous Vehicle (AV). Autonomous vehicle is a driverless vehicle which able to drive on its own. It works with the aid of various embedded systems and sensors that help the AV to function greatly. Speed limit signs recognition is one of the TSR essential features for AV where it helps to automatically detect and recognize speed limit signs on the road. There are many speed limit signs available on the road including 30km/h, 60km/h, 90km/h signs and to name a few. However, the interclass similarity among these speed limit signs has created a challenge for the TSR system in detection and recognition process. Therefore, this study proposes image processing technique to develop the speed limit sign recognition for TSR system utilizing a single layer network called Single Shot Multibox Detector (SSD) algorithm. The German Traffic Sign Dataset (GTSD) is used for the purpose of training the model and the model is then tested using the real-time images of standard Malaysian speed limit signs. An accuracy testing using confusion matrix is conducted to find the overall accuracy of the system. A total 100 images are used during testing and the system achieved over 92.4% of the average accuracy for detection and recognition of the speed limit signs.
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