Handling uncertainty in optimization enabled the deep Q network for traffic sign and criticality detection

OBJECTIVE: This research aims to develop an efficient model for Traffic Sign Recognition (TSR) with uncertainty quantification. METHODS: The process involves pre-processing, sign localization, TSR, and criticality detection. After pre-processing with a Gaussian filter, sign localization is carried out using Segmentation U-Net (SegU-Net). Following TSR, a Deep Q Network (DQN) is trained using Gradient Descent-Teamwork Optimization Algorithm-based Deep Q Network (GD-TOA based DQN), which incorporates Gradient Descent model into Teamwork Optimization Algorithm (TOA), is used to classify traffic signs into three categories: mandatory, cautionary. and informatory. After classification, the system revalidates critical signs using Mahalanobis distance to compute confidence levels. A significant drop triggers a flag for human review if it has a high uncertainty. RESULTS: Empirical findings demonstrate that presented methodology achieved an accuracy rate of 97.9%, a precision rate of 98.2%, a recall rate of 98.3%, F-measure of 98.2%, specificity of 97.5%, and balanced accuracy of 97.9%. CONCLUSIONS: The results have proven that proposed model is a very promising technique for criticality detection.