Effective Detection and Recognition of Traffic Signs with Light Convolutional Neural Networks

Detection and recognition of traffic signs are two analytic processes in vehicular systems that contribute to increasing driver safety, warning, and preventing collisions by improving drivers’ focus and awareness. They are also crucial for developing self-driving vehicles that can sense the environment through a camera eye and understand the road restrictions. Convolutional Neural Networks (CNNs) play an essential role in both processes by finding the traffic sign objects on acquired images or video frames and recognizing their meaning. However, CNN architectures running on vehicles need minimization, ensuring satisfactory performance and efficient operation with decreased computational resources. In this paper, we investigate two CNN architectures for traffic sign detection and two architectures for traffic sign recognition. Our experiments confirm that light models for both processes can successfully perform the achieved tasks, reaching effectiveness close to the complex models reported in the scientific literature.