Traffic Sign Detection for Real-World Application Using Hybrid Deep Belief Network Classification

Abstract

By integrating automated driving systems (ADS) and AI-driven advanced driver assistance systems (ADAS) like the traffic sign detection (TSD) technology, the automotive sector can develop smart and self-driving cars. Traffic signs (TS) play a crucial role in avoiding accidents and traffic congestion. Motorists need to understand the visual representations of various data elements incorporated in traffic symbols. There are often instances where drivers neglect TS located ahead of their vehicles, resulting in severe outcomes. This research offers an automatic TSD forecast utilising the hybrid deep belief network (HDBN) model for classification to address this issue. When it comes to forecasting the future world of smart urban cities, the given HDBN model primarily focuses on high-precision traffic prediction. The rider sunflower optimization (RSFO) technique is utilised to improve the hyper parameter tuning, which improves the overall effectiveness of the traffic flow prediction process. Overall, the suggested TSD system is found to be a highly efficient method of detecting TS, performing exceptionally well in relation to precision, recall, accuracy, and F1. The suggested solution under evaluation appears to perform better in terms of accuracy than other current methods stated in this chapter.