Low-Light Image Enhancement Framework for Improved Object Detection in
  Fisheye Lens Datasets
Dai Quoc Tran Armstrong Aboah Yuntae Jeon Maged Shoman Minsoo Park Seunghee Park
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.