Traffic sign detection based on cascaded convolutional neural networks
Di Zang, Junqi Zhang, Dongdong Zhang, Maomao Bao, Jiujun Cheng, Keshuang Tang · 2016
In this paper, we present a new approach to detect traffic signs based on cascaded convolutional neural networks (CNNs). First, the local binary pattern (LBP) feature detector and the AdaBoost classifier are combined to extract regions of interest (ROI) for coarse selection. Next, cascaded CNNs are employed to reduce negative samples of ROI for traffic sign recognition. Compared with the conventional CNN, our CNN contains three convolutional layers and its classification part is replaced by the support vector machine (SVM). The German traffic sign detection benchmark is used and experimental results demonstrate that the proposed method can achieve competitive results when compared with the state-of-the-art approaches.