Attention-based two-stream high-resolution networks for building damage assessment from satellite imagery

Oludare, Victor; Kezebou, Landry; Jinadu, Obafemi; Panetta, Karen; Agaian, Sos S.

doi:10.1117/12.2618901

Cited by 2 publications

(1 citation statement)

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“…However, the effectiveness of these systems is currently compromised in challenging visual conditions. Critical tasks such as object classification, detection, tracking, segmentation, [1][2][3][4] vehicle speed estimation, 5 and automated surveillance perform sub-optimally under low-light conditions-situations commonly encountered during night-time or in poorly lit environments. These images are often compromised in adverse lighting conditions characterized by low light, uneven illumination, insufficient exposure, or narrow aperture settings.…”

Section: Introductionmentioning

confidence: 99%

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

Jinadu,

Rajeev,

Panetta

et al. 2024

Multimodal Image Exploitation and Learning 2024

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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. The project Github repository can be accessed here.

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Section: Introductionmentioning

confidence: 99%