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An essential requirement for driver assistance systems and autonomous drivingtechnology is implementing a robust system for detecting and recognizingtraffic signs. This system enables the vehicle to autonomously analyze theenvironment and make appropriate decisions regarding its movement, even whenoperating at higher frame rates. However, traffic sign images captured ininadequate lighting and adverse weather conditions are poorly visible, blurred,faded, and damaged. Consequently, the recognition of traffic signs in suchcircumstances becomes inherently difficult. This paper addressed the challengesof recognizing traffic signs from images captured in low light, noise, andblurriness. To achieve this goal, a two-step methodology has been employed. Thefirst step involves enhancing traffic sign images by applying a modified MIRNetmodel and producing enhanced images. In the second step, the Yolov4 modelrecognizes the traffic signs in an unconstrained environment. The proposedmethod has achieved 5.40% increment in mAP@0.5 for low quality images onYolov4. The overall mAP@0.5 of 96.75% has been achieved on the GTSRB dataset.It has also attained mAP@0.5 of 100% on the GTSDB dataset for the broadcategories, comparable with the state-of-the-art work.

Automatic Signboard Recognition in Low Quality Night Images