Negative-Supervised Cascaded Deep Learning for Traffic Sign Classification

In this paper, we propose a novel deep learning framework for object classification called negative-supervised cascaded deep learning. There are two hierarchies in our cascaded method: the first one is a convolutional neural network trained on positive-only samples, which is used to select supervisory samples from a negative library. The second one is inherited from the trained first CNN. It is trained on positive and negative samples, which are selected from domain related database by utilizing negative-supervised mechanism. Experiments are applied this idea to traffic sign classification using two classic convolutional neural networks, LeNet-5 and AlexNet as baselines. Classification rates improved by \(0.7\%\) and \(1.1\%\) with LeNet-5 and AlexNet respectively, which demonstrates the efficiency and superiority of our proposed framework.