在低光环境中使用深度学习图像增强的双阶段对象检测.
Ghaith Al-Refai1, Hisham Elmoaqet1, Abdullah Al-Refai2
1Department of Mechatronics Engineering, German Jordanian University, Amman, Jordan.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了在低光图像中对象检测的两阶段系统. 该系统通过使用深度学习首先提高图像质量,然后再应用对象检测算法来显著提高检测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像处理 图像处理
背景情况:
- 由于图像质量差,在低光条件下对象检测存在重大挑战.
- 现有的方法往往难以在照明不足的环境中准确识别物体.
研究的目的:
- 开发和评估一个针对低光环境优化的两阶段物体检测系统.
- 评估不同图像增强技术对后续物体检测性能的影响.
主要方法:
- 一种两阶段的方法,将监督深度学习用于图像增强和计算机视觉算法用于对象检测相结合.
- 评估了三个增强算法 (ZeroDCE++,Gladnet,TBEFN) 和YOLOv7用于ExDark数据集上的检测.
- 使用无参考图像质量评估器和物体检测指标 (回忆,平均平均精度) 的评估.
主要成果:
- 带有TBEFN增强的两级系统实现了0.574的平均平均精度 (mAP),单独超过YOLOv7 (mAP 0.49).
- 无参考图像质量评估器 (NIQE) 与对象检测性能 (mAP) 显示出强烈的相关性.
结论:
- 两级物体检测系统在低光条件下显著提高了性能.
- 像NIQE这样的图像质量评估指标可以预测和告知计算机视觉任务的改进.
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