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A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet-5 Network
Journal of Sensors ( IF 1.9 ) Pub Date : 2021-04-30 , DOI: 10.1155/2021/8870529
Ameur Zaibi 1, 2 , Anis Ladgham 1, 3 , Anis Sakly 1, 2
Affiliation  

For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet-5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet-5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.

中文翻译:

基于增强型LeNet-5网络的交通标志分类轻量模型

几年来,许多研究都集中在交通标志识别系统的重要性上,交通标志识别系统在道路安全中起着非常重要的作用。研究人员已经利用机器学习,深度学习和图像处理技术成功地进行了研究。道路标志分类和识别系统的最新研究是使用基于深度学习的架构(例如卷积神经网络(CNN)架构)的结果。在这项研究工作中,目标是要实现一种轻巧的CNN模型,该模型对于嵌入式应用程序很容易实现,并且具有出色的分类精度。我们选择使用改进的LeNet-5网络模型对路标进行分类。我们在德国交通标志识别基准(GTSRB)数据库和比利时交通标志数据集(BTSD)上训练了模型网络,与我们测试的其他模型和其他研究人员测试的其他模型相比,该模型网络提供了良好的结果。GTSRB的准确性为99.84%,BTSD的准确性为98.37%。基于增强的LeNet-5网络,模型的轻巧性和减少的参数数量(38万个)促使我们使用网络摄像头为嵌入式应用程序测试模型。我们发现的结果是有效的,强调了我们方法的有效性。基于增强的LeNet-5网络,模型的轻巧性和减少的参数数量(38万个)促使我们使用网络摄像头为嵌入式应用程序测试模型。我们发现的结果是有效的,强调了我们方法的有效性。基于增强的LeNet-5网络,模型的轻巧性和减少的参数数量(38万个)促使我们使用网络摄像头为嵌入式应用程序测试模型。我们发现的结果是有效的,强调了我们方法的有效性。
更新日期:2021-04-30
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