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

• URL: http://arxiv.org/abs/2404.10078v1
• Date: Mon, 15 Apr 2024 18:32:52 GMT
• Title: Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
• Authors: Dai Quoc Tran, Armstrong Aboah, Yuntae Jeon, Maged Shoman, Minsoo Park, Seunghee Park,
• Abstract summary: This study addresses the evolving challenges in urban traffic monitoring systems based on fisheye lens cameras. Fisheye lenses provide wide and omnidirectional coverage in a single frame, making them a transformative solution. Motivated by these challenges, this study proposes a novel approach that combines a ransformer-based image enhancement framework and ensemble learning technique.
• Score: 4.170227455727819
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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