Affiliations: [a] School of Electronic and Information Engineering, Hebei University of Technology, China
| [b] School of Computer Science and Engineering, Hebei University of Technology, China
| [c] North Information Control Group Co,. Ltd, China
Correspondence:
[*]
Corresponding author. Ming Yu, School of Computer Science and Engineering, Hebei University of Technology, China. Tel.: +86 13702173627; E-mail: [email protected].
Abstract: Hazy weather affects drivers’ sightline seriously and causes a high potential safety hazard. This paper proposes a novel approach for recognizing the speed limit sign in hazy weather. It consists of three major modules: haze removal, speed limit sign location, and sign recognition. In haze removal, this paper proposes to dehaze image with the dark channel prior. The speed limit sign is located by Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) classification and is recognized by the seven layers Convolutional Neural Networks (CNN). Experimental results show that the proposed method has better performance than the state-of-art dehazing methods and the processing time is also reduced. The recognition rate of the speed limit signs is 98.51% that is better than the human performance, and the classifier can recognize the speed limit sign with rotation, shift, scale and other distortions.
Keywords: Computer vision and pattern recognition, speed limit sign, haze removal, dark channel prior, HOG, CNN
