CapsNet-based Precise and Rapid Traffic Sign Detection through AI in Adverse Environmental Scenarios

This research presents an innovative system for real-time recognition of traffic signs, leveraging Artificial Intelligence (AI) to achieve high-performance identification, particularly under challenging conditions prevalent in traffic scenarios such as occlusions, atmospheric haze, and noise. The proposed system employs an advanced Capsule Network (CapsNet) algorithm for object recognition, trained extensively on a diverse dataset encompassing images of various traffic signs. Notably, the system demonstrates concurrent detection capabilities for multiple traffic signals, accurately categorizing them based on their respective classes. To address distortions caused by alterations in the camera's perspective, including rotation, torsion, and elongation, the system effectively employs techniques ensuring precise alignment with the pertinent traffic sign. Furthermore, pre-processing techniques are utilized to resolve ambiguity and distortions in complex traffic scenarios. Empirical validation of the proposed methodology is conducted through experimentation with authentic traffic sign images obtained from diverse environmental contexts. Comparative assessments across diverse datasets representing prominent traffic sign domains affirm the efficacy of the proposed approach. The outcomes showcase a noteworthy precision level, achieving a recognition accuracy of 99.16% for traffic signs. In contrast, conventional rule-based systems under identical conditions exhibit accuracy rates ranging between 80–90%. The AI-driven system demonstrates real-time operational feasibility, positioning it as a fitting candidate for applications in traffic management and intelligent transportation systems.