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Traffic signs recognition (TSR) is an important part of some advanced driver-assistance systems (ADASs) and auto driving systems (ADSs). As the first key step of TSR, traffic sign detection (TSD) is a challenging problem because of different types, small sizes, complex driving scenes, and occlusions. In recent years, there have been a large number of TSD algorithms based on machine vision and pattern recognition. In this paper, a comprehensive review of the literature on TSD is presented. We divide the reviewed detection methods into five main categories: color-based methods, shape-based methods, color-and shape-based methods, machine-learning-based methods, and LIDAR-based methods. The methods in each category are also classified into different subcategories for understanding and summarizing the mechanisms of different methods. For some reviewed methods that lack comparisons on public datasets, we reimplemented part of these methods for comparison. The experimental comparisons and analyses are presented on the reported performance and the performance of our reimplemented methods. Furthermore, future directions and recommendations of the TSD research are given to promote the development of the TSD.

This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common fea- tures of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information.Index Terms-Common-Finder AdaBoost (CF.AdaBoost), multiblock normalization local binary pattern (MN-LBP), multiclass object detection, split-flow cascade, traffic sign detection (TSD).