An impact study of deep learning-based low-light image enhancement in intelligent transportation systems

Obafemi Jinadu; Srijith Rajeev; Karen Panetta; Sos S. Agaian

doi:10.1117/12.3014452

7 June 2024 An impact study of deep learning-based low-light image enhancement in intelligent transportation systems

Obafemi Jinadu, Srijith Rajeev, Karen Panetta, Sos S. Agaian

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330K (2024) https://doi.org/10.1117/12.3014452
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Images and videos captured in poor illumination conditions are degraded by low brightness, reduced contrast, color distortion, and noise, rendering them barely discernable for human perception and ultimately negatively impacting computer vision system performance. These challenges are exasperated when processing video surveillance camera footage, using this unprocessed video data as-is for real-time computer vision tasks across varying environmental conditions within Intelligent Transportation Systems (ITS), such as vehicle detection, tracking, and timely incident detection. The inadequate performance of these algorithms in real-world deployments incurs significant operational costs. Low-light image enhancement (LLIE) aims to improve the quality of images captured in these unideal conditions. Groundbreaking advancements in LLIE have been recorded employing deep-learning techniques to address these challenges, however, the plethora of models and approaches is varied and disparate. This paper presents an exhaustive survey to explore a methodical taxonomy of state-of-the-art deep learning-based LLIE algorithms and their impact when used in tandem with other computer vision algorithms, particularly detection algorithms. To thoroughly evaluate these LLIE models, a subset of the BDD100K dataset, a diverse real-world driving dataset is used for suitable image quality assessment and evaluation metrics. This study aims to provide a detailed understanding of the dynamics between low-light image enhancement and ITS performance, offering insights into both the technological advancements in LLIE and their practical implications in real-world conditions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Obafemi Jinadu, Srijith Rajeev, Karen Panetta, and Sos S. Agaian "An impact study of deep learning-based low-light image enhancement in intelligent transportation systems", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330K (7 June 2024); https://doi.org/10.1117/12.3014452

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