Real-time object detection of traffic signs using SSDNet: a case study in Shanghai

The objective detection of traffic signs in scene images is a research problem that entails identifying traffic sign targets within a given visual context. In recent years, this pursuit has found extensive applications in road scene monitoring systems and autonomous driving technologies. Consequently, the optimization of traffic sign object detection holds paramount significance. This study focuses on establishing a model for real-time detection of traffic signs in the urban environment of Shanghai. Leveraging a dedicated traffic sign dataset, training was conducted utilizing the SSD (Single Shot Multibox Detector) neural network for object detection. The results indicate an achieved error rate of approximately 5%. With few exceptions related to challenging lighting conditions or instances where the size of the traffic signs proved to be excessively small, the model demonstrated favorable outcomes across the majority of images.