RDD-YOLOv5:基于无人驾驶飞行器检查的自我注意的道路缺陷检测算法
Yutian Jiang1, Haotian Yan1, Yiru Zhang2
1College of Transportation, Jilin University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
一个新的深度学习模型,道路缺陷检测YOLOv5 (RDD-YOLOv5),显著提高了道路裂检测的准确性. 这种先进的算法通过克服复杂的背景和低分辨率图像的挑战来提高道路维护和安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 手动检测道路缺陷是低效和不准确的.
- 深度学习提供了潜力,但面临着裂检测复杂性的挑战.
- 现有的方法在各种背景,低分辨率和裂纹相似性方面扎.
研究的目的:
- 开发一种新的,高度准确的道路裂检测算法.
- 为了提高现有的YOLOv5模型的性能,用于道路缺陷识别.
- 提高道路维护检查的效率和可靠性.
主要方法:
- 提出了一种新的道路缺陷检测YOLOv5 (RDD-YOLOv5) 算法.
- 集成的变压器结构和明确的视觉中心,用于增强功能捕获.
- 用高斯误差线性单位取代西格莫伊德加权的线性激活,以改进适配.
- 使用无人机飞行平台进行实验评估.
主要成果:
- RDD-YOLOv5实现了91.48%的平均平均精度.
- 拟议的模型的性能比原来的YOLOv5优于2.5%.
- 在复杂的交通环境中证明了准确的道路裂识别.
结论:
- RDD-YOLOv5在自动化道路裂检测方面取得了重大进展.
- 该模型有效地解决了复杂的背景和图像质量所带来的挑战.
- 这项技术有望提高道路维护策略和公共安全.
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