# Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets

arXiv: https://arxiv.org/abs/2404.10078 · HTML: https://mlsanity.com/paper/2404.10078/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [Dual Aggregation Transformer for Image Super-Resolution](https://mlsanity.com/paper/2308.03364/) (2023)
- [Global Structure-Aware Diffusion Process for Low-Light Image Enhancement](https://mlsanity.com/paper/2310.17577/) (2023)
- [NAFSSR: Stereo Image Super-Resolution Using NAFNet](https://mlsanity.com/paper/2204.08714/) (2022)
- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection](https://mlsanity.com/paper/2305.17449/) (2023)
- [Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8](https://mlsanity.com/paper/2304.08256/) (2023)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis](https://mlsanity.com/paper/2404.08229/) (2024)
- [Artificial Intelligence Enabled Traffic Monitoring System](https://mlsanity.com/paper/2010.01217/) (2020)
- [DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos](https://mlsanity.com/paper/2304.08261/) (2023)
- [An Extendable, Efficient and Effective Transformer-based Object Detector](https://mlsanity.com/paper/2204.07962/) (2022)
- [Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5](https://mlsanity.com/paper/2304.09248/) (2023)
- [GC-GRU-N for Traffic Prediction using Loop Detector Data](https://mlsanity.com/paper/2211.08541/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic Surveillance](https://mlsanity.com/paper/2510.20016/) (2025)
- [BioDet: Boosting Industrial Object Detection with Image Preprocessing Strategies](https://mlsanity.com/paper/2510.21000/) (2025)
- [Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning](https://mlsanity.com/paper/2511.17183/) (2025)

## Abstract (arXiv)

This study addresses the evolving challenges in urban traffic monitoring detection systems based on fisheye lens cameras by proposing a framework that improves the efficacy and accuracy of these systems. In the context of urban infrastructure and transportation management, advanced traffic monitoring systems have become critical for managing the complexities of urbanization and increasing vehicle density. Traditional monitoring methods, which rely on static cameras with narrow fields of view, are ineffective in dynamic urban environments, necessitating the installation of multiple cameras, which raises costs. Fisheye lenses, which were recently introduced, provide wide and omnidirectional coverage in a single frame, making them a transformative solution. However, issues such as distorted views and blurriness arise, preventing accurate object detection on these images. Motivated by these challenges, this study proposes a novel approach that combines a ransformer-based image enhancement framework and ensemble learning technique to address these challenges and improve traffic monitoring accuracy, making significant contributions to the future of intelligent traffic management systems. Our proposed methodological framework won 5th place in the 2024 AI City Challenge, Track 4, with an F1 score of 0.5965 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. Our code is publicly available at https://github.com/daitranskku/AIC2024-TRACK4-TEAM15.