一个微调的YOLOv5深度学习方法用于实时的房屋号码检测
Murat Taşyürek1, Celal Öztürk2
1Department of Computer Engineering, Kayseri University, Kayseri, Turkey.
PeerJ. Computer science
|August 7, 2023
概括
这项研究使用微调卷积神经网络 (CNN) 模型增强了自然图像中的房屋号码检测. 微调的YOLOv5模型实现了最高的性能,显著提高了小型可变物体的检测精度.
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.
- 对象检测检测器可以检测物体.
背景情况:
- 在自然图像中检测像房子号码这样的小物体是具有挑战性的,因为模糊和深度.
- 卷积神经网络 (CNN) 模型在对象检测中很普遍,但与小的,尺寸可变的对象作斗争.
研究的目的:
- 改进从自然图像中实时检测房屋号码.
- 为了评估微调常见的CNN模型对此任务的有效性.
主要方法:
- 应用了经典的CNN模型:更快的R-CNN,移动网络,YOLOv4,YOLOv5和YOLOv7.
- 引入了一种新的微调技术,以提高这些CNN模型的性能.
- 在凯塞里省的现实数据上进行了实验.
主要成果:
- 微调改进了所有测试的CNN模型的F1分数.
- 精心调整的YOLOv5获得了最高的F1分数 (0.972),超过了其他车型.
- YOLOv7仍然是最快的模型 (0.009秒),但微调的YOLOv5提供了更高的准确性.
结论:
- 微调是增强基于CNN的房屋号码检测的有效策略.
- 拟议的微调YOLOv5方法提供了高精度和可接受速度的强大平衡.
- 这项研究有助于为自然场景提供更强大的实时物体检测系统.
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