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

TL;DR

This paper introduces a novel image enhancement framework combining transformer-based methods and ensemble learning to improve object detection accuracy in fisheye lens images for urban traffic monitoring.

Contribution

It proposes a new approach that addresses fisheye image distortions, enhancing detection accuracy and robustness, and demonstrates competitive performance in a major AI challenge.

Findings

Achieved 5th place in the 2024 AI City Challenge with an F1 score of 0.5965

Demonstrated improved detection accuracy on fisheye images

Validated the effectiveness and robustness of the proposed system

Abstract

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.