DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements

For a safe and automated vehicle driving application, it is a prerequisite to have a robust and highly accurate traffic sign detection system. In this paper, we proposed a novel energy-efficient Thin yet Deep convolutional neural network architecture for traffic sign recognition. Within the proposed architecture, each convolutional layer contains less than 50 features enabling our convolutional neural network to be trained quickly even without the aid of a graphics processing unit. The performance of the proposed architecture is measured using two publicly available traffic sign datasets, namely the German Traffic Sign Recognition Benchmark and the Belgian Traffic Sign Classification dataset. First, we train and test the performance of the proposed architecture using the large German Traffic Sign Recognition Benchmark…