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Handling uncertainty in optimization enabled the deep Q network for traffic sign and criticality detection
Soja Salim1,2, Jayasudha J S3, Soniya B1
1Computer Science and Engineering, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram, India.
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.
