A Hybrid Connecting Character Based Text Recognition and Extraction Algorithm

Traffic sign recognition is a technology by which a vehicle is able to recognize the traffic signs put on the road e.g. "speed limit" or "children" or "turn ahead". In this paper a novel Connecting Character based text recognition and extraction algorithm is designed which uses Maximally Stable Extremely Regions (MSER) for test candidate recognition and extraction from traffic signs. Despite their auspicious properties, MSER has been conveyed to be delicate towards blurred Image. To allow for detecting small letters in images of limited resolution or blurred Image, the complimentary properties of Lucy-Richardson Algorithm and canny edge Algorithm is used

CIRCULAR TRAFFIC SIGN CLASSIFICATION USING HOGBASED RING PARTITIONED MATCHING

This paper presents a technique to classify the circular traffic sign based-on HOG (histogram of oriented gradients) and a ring partitioned matching. The method divides an image into several ring areas, and calculates the HOG feature on each ring area. In the matching process, the weight is assigned to each ring for calculating the distance of HOG feature between tested image and reference image. The experimental results show that the proposed algorithm achieves a high classification rate of 97.8%, without the need of many prepared sample images. The results also show that the best values of the number of orientation bins and the cell size of the HOG parameters are 5 and 10 x 10 pixels respectively. Index terms: HOG, traffic sign classification, ring partitioned, template matching

Traffic Sign and Light Detection using Deep learning for Automotive Applications

Traffic sign and light detection are core components of Advanced Driver Assistance Systems (ADAS) and self-driving vehicles. The automotive industry is widely employing numerous approaches for automation through computer vision techniques. Object detection algorithms based on deep learning can be divided into two main categories, two stage and single stage detection algorithms. Two stage algorithms are designed to improve detection accuracy. While single stage algorithms are constructed to be faster, this increases their suitability for real time applications. This thesis presents a lightweight traffic sign and light detector by adapting a single stage, Single Shot Multibox Detection (SSD) algorithm by providing both high accuracy and real time detection capability. Therefore, the Visual geometry group (VGG16) base network in original SSD is replaced by MobileNet, that expertly manages detection speed and network size because of its lighter architecture. It is essential for the application domain to be able to detect small objects which is what the original SSD struggles with. For autonomous driving the detection results with respect to the distance of an object is of particular interest. A comfortable braking distance is needed in case of traffic signs and lights. That requires object detection from a farther distance, but farther distance makes the object to be detected appear smaller. Thus, this work further optimizes the number of feature map layers of the algorithm for the detection of small objects along with a better trade off between accuracy and detection time. Experimental results confirm the effectiveness of the proposed model as compared to the standard SSD with VGG16 and SSD with MobileNet V2