论文标题
使用深度学习的交通标志检测和识别系统
Traffic Signs Detection and Recognition System using Deep Learning
论文作者
论文摘要
随着技术的快速发展，汽车已成为我们日常生活中的重要资产。最重要的研究之一是交通标志识别（TSR）系统。本文介绍了一种通过转移学习手段实时地实时检测和识别交通标志的方法。我们使用多物体检测系统的最新技术（例如更快的复发性卷积神经网络（F-RCNN））和单个Shot Multi-Box探测器（SSD）以及各种功能提取器（例如Mobilenet v1 v1 v1 and inception v2 v2）以及Tiny-Yolov2解决。但是，本文的重点将是F-RCNN Inception V2和Tiny Yolo V2，因为它们取得了最佳结果。在德国交通标志检测基准（GTSDB）数据集上对上述模型进行了微调。这些模型在主机PC以及Raspberry Pi 3型B+和Tass Prescan模拟上进行了测试。我们将在结论部分中讨论所有模型的结果。
With the rapid development of technology, automobiles have become an essential asset in our day-to-day lives. One of the more important researches is Traffic Signs Recognition (TSR) systems. This paper describes an approach for efficiently detecting and recognizing traffic signs in real-time, taking into account the various weather, illumination and visibility challenges through the means of transfer learning. We tackle the traffic sign detection problem using the state-of-the-art of multi-object detection systems such as Faster Recurrent Convolutional Neural Networks (F-RCNN) and Single Shot Multi- Box Detector (SSD) combined with various feature extractors such as MobileNet v1 and Inception v2, and also Tiny-YOLOv2. However, the focus of this paper is going to be F-RCNN Inception v2 and Tiny YOLO v2 as they achieved the best results. The aforementioned models were fine-tuned on the German Traffic Signs Detection Benchmark (GTSDB) dataset. These models were tested on the host PC as well as Raspberry Pi 3 Model B+ and the TASS PreScan simulation. We will discuss the results of all the models in the conclusion section.
