Two-stage object detection in low-light environments using deep learning image enhancement

Al-refai, G, Elmoaqet, H, Al-Refai, A, Alzu’bi, A, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X and Alkhateeb, A, 2025. Two-stage object detection in low-light environments using deep learning image enhancement. PeerJ Computer Science, 11: e2799. ISSN 2376-5992

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Abstract

This study presents a two-stage object detection system specifically tailored for low-light conditions. In the initial stage, supervised deep learning image enhancement techniques are utilized to improve image quality and enhance features. The second stage employs a computer vision algorithm for object detection. Three image enhancement algorithms—ZeroDCE++, Gladnet, and two-branch exposure-fusion network for low-light image enhancement (TBEFN)—were assessed in the first stage to enhance image quality. YOLOv7 was utilized in the object detection phase. The ExDark dataset, recognized for its extensive collection of low-light images, served as the basis for training and evaluation. No-reference image quality evaluators were applied to measure improvements in image quality, while object detection performance was assessed using metrics such as recall and mean average precision (mAP). The results indicated that the two-stage system incorporating TBEFN significantly improved detection performance, achieving a mAP of 0.574, compared to 0.49 for YOLOv7 without the enhancement stage. Furthermore, this study investigated the relationship between object detection performance and image quality evaluation metrics, revealing that the image quality evaluator NIQE exhibited a strong correlation with mAP for object detection. This correlation aids in identifying the features that influence computer vision performance, thereby facilitating its enhancement.

Item Type:	Journal article
Publication Title:	PeerJ Computer Science
Creators:	Al-refai, G., Elmoaqet, H., Al-Refai, A., Alzu’bi, A., Al-Hadhrami, T. and Alkhateeb, A.
Publisher:	PeerJ
Date:	7 April 2025
Volume:	11
ISSN:	2376-5992
Identifiers:	Number Type 10.7717/peerj-cs.2799 DOI 2424381 Other
Rights:	This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	15 Apr 2025 09:42
Last Modified:	15 Apr 2025 09:42
URI:	https://irep.ntu.ac.uk/id/eprint/53410

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