Computational methods for automatic traffic signs recognition in autonomous driving on road: A systematic review - Hui Chen; Mohammed A.H. Ali; Yusoff Nukman; Bushroa Abd Razak; Sherzod Turaev; YiHan Chen; Shikai Zhang; Zhiwei Huang; Zhenya Wang; Rawad Abdulghafor (Results in Engineering)
This review discusses the progress made in the traffic-sign detection and recognition methods and algorithms over the last decade with analyzing the strengths and drawbacks of each algorithm. The recent development of traffic sign recognition on the roads highlights the necessity for precise detection of road's traffic signs in various driving scenarios. In addition, the connections between the detection algorithms before and after the advent of deep learning are revealed. The Traffic sign recognition has been developed to identify various shapes, sizes, orientations, and appearances of signs in diverse conditions. Researchers have proposed numerous algorithms to address these challenges. The traffic recognition methods have been categorized in this paper into three main techniques, namely, conventional, deep learning, and hybrid based methods. The algorithms are compared with each others via regression, segmentation, and hybrid techniques, specifically SSD, YOLO, Faster R-CNN, Pixel Aggregation Network, and Mask R-CNN. The results demonstrate that the hybrid based detection algorithms outperform others in true-positive rates, false-positive rates, the number of test images, accuracy, and processing time. Such outcomes illustrate the potential of hybrid methods in the creation of accurate and effective TSD systems, thereby paving the way for further research in this field.