Automatic Signboard Recognition in Low Quality Night Images
Manas Kagde Priyanka Choudhary Rishi Joshi Somnath Dey
An essential requirement for driver assistance systems and autonomous driving
technology is implementing a robust system for detecting and recognizing
traffic signs. This system enables the vehicle to autonomously analyze the
environment and make appropriate decisions regarding its movement, even when
operating at higher frame rates. However, traffic sign images captured in
inadequate lighting and adverse weather conditions are poorly visible, blurred,
faded, and damaged. Consequently, the recognition of traffic signs in such
circumstances becomes inherently difficult. This paper addressed the challenges
of recognizing traffic signs from images captured in low light, noise, and
blurriness. To achieve this goal, a two-step methodology has been employed. The
first step involves enhancing traffic sign images by applying a modified MIRNet
model and producing enhanced images. In the second step, the Yolov4 model
recognizes the traffic signs in an unconstrained environment. The proposed
method has achieved 5.40% increment in mAP@0.5 for low quality images on
Yolov4. 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 broad
categories, comparable with the state-of-the-art work.