DANG Qian2， MA Miao1,2,3*， CHEN Yuli1,2
(1 Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi′an 710062, Shaanxi, China;2 School of Computer Science, Shaanxi Normal University, Xi′an 710119, Shaanxi, China;3 Shaanxi Key Lab of Speech & Image Information Processing, Xi′an 710072, Shaanxi, China)
Abstract:
Focusing on GTSRB dataset acquired in real world, a traffic sign recognition algorithm based on the 2-level improved LeNet-5 is proposed，which combines convolutional neural networks with support vector machines. With the consideration of the requirement of real-time recognition, the traditional network structure of LeNet-5 is improved first. After GTSRB dataset images were cropped and converted to grayscale images, their brightness and size are normalized to 32×32 images. Next, a 2-level improved LeNet-5 is trained with GTSRB dataset, where the first level categorized traffic signs to 6 categories with the improved LeNet-5, and the second level improved LeNet-5 provide with the final category. Experimental results show that the proposed algorithm could provide with a correct recognition ratio 91.76%, since the multi-scale features could be fully analyzed with 2-level improved LeNet-5.
KeyWords:
convolutional neural networks; traffic signs; classification; support vector machine
