# Automatic Signboard Recognition in Low Quality Night Images

arXiv: https://arxiv.org/abs/2308.08941 · HTML: https://mlsanity.com/paper/2308.08941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)

## Abstract (arXiv)

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.