Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Abstract

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

“…Thus, the procedure of the TSDR system can be divided into three stages, the detection, tracking and classification stages. Detection is concerned with locating traffic signs in the input scene images, whereas classification is about determining what type of sign the system is looking at [17,18]. In other words, traffic sign detection involves generating candidate region of interests (ROIs) that are likely to contain regions of traffic signs, while traffic sign classification gets each candidate ROI and tries to identify the exact type of sign or rejects the identified ROI as a false detection [4,19].…”

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

“…The characters and digits of the rows were segmented using vertical projection with the threshold value. Finally, template matching was used for recognizing [22], [23], [24] the characters and digits of the license plate. Wang et al [25] used the Roberts operator for edge detection and morphological erosion, image clustering and region filling for plate detection from complex backgrounds.…”

Neural-Network-Based Traffic Sign Detection and Recognition in High-Definition Images Using Region Focusing and Parallelization

“…Moreover, it is noticeable that although several papers report real-time TSD/TSR [23]- [25], none of them considered realtime detection and recognition in HD images entirely based on CNN. In [17], [26], real-time detection is reported, where detection is done by image filtering while CNN is used for recognition only.…”