Al-Refai Ghaith, Elmoaqet Hisham, Al-Refai Abdullah, Alzu'bi Ahmad, Al-Hadhrami Tawfik, Alkhateeb Abedalrhman
Department of Mechatronics Engineering, German Jordanian University, Amman, Jordan.
Software Engineering Department, King Hussein School of Computing Science, Princess Sumaya University College for Technology, Amman, Jordan.
PeerJ Comput Sci. 2025 Apr 7;11:e2799. doi: 10.7717/peerj-cs.2799. eCollection 2025.
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.
本研究提出了一种专门针对低光照条件定制的两阶段目标检测系统。在初始阶段，利用有监督的深度学习图像增强技术来提高图像质量并增强特征。第二阶段采用计算机视觉算法进行目标检测。在第一阶段评估了三种图像增强算法——ZeroDCE++、Gladnet和用于低光照图像增强的双分支曝光融合网络（TBEFN），以提高图像质量。在目标检测阶段使用了YOLOv7。以其大量低光照图像集而闻名的ExDark数据集用作训练和评估的基础。使用无参考图像质量评估器来衡量图像质量的提升，同时使用召回率和平均精度均值（mAP）等指标评估目标检测性能。结果表明，结合TBEFN的两阶段系统显著提高了检测性能，mAP达到0.574，而没有增强阶段的YOLOv7的mAP为0.49。此外，本研究调查了目标检测性能与图像质量评估指标之间的关系，发现图像质量评估器NIQE与目标检测的mAP呈现出很强的相关性。这种相关性有助于识别影响计算机视觉性能的特征，从而促进其提升。
