一个水下裂检测系统,结合了新的水下图像处理技术和改进的YOLOv9网络
Xinbo Huang1, Chenxi Liang2, Xinyu Li3
1School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
检测水下裂是一个挑战. 本研究介绍了一种图像处理技术和改进的YOLOv9-OREPA模型,用于将水下图像转换为清晰的水面图像,以准确检测水中的裂.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 水下裂检测对于基础设施的完整性至关重要,但受到图像质量挑战的阻碍.
- 现有的深度学习方法需要广泛的,难以获得的水下裂数据集.
- 危险的水下环境使得用于裂监测的数据采集复杂化.
研究的目的:
- 通过提高图像质量,开发一种有效的水下裂检测方法.
- 调整深度学习模型,以提高处理水下裂纹图像的性能.
- 提供强大的解决方案,用于检测水下水结构的裂.
主要方法:
- 采用了新的白平衡和双边过去色方法来进行水下图像增强.
- 水下裂纹图像被转化为高质量的水面对应物,保留原始特征.
- 用一个改进的YOLOv9-OREPA模型来检测增强图像上的裂.
主要成果:
- 与现有方法相比,拟议的图像处理技术显著改善了图像质量指标.
- 增强的图像促进了改进的YOLOv9-OREPA模型的卓越性能.
- 实验结果验证了水下裂检测综合方法的有效性.
结论:
- 开发的方法提供了一种新的方法,可以将具有挑战性的水下裂图像转换为可用的水面格式.
- 该技术提高了基于深度学习的水下基础设施裂检测的可行性和准确性.
- 该研究成功地展示了用于检测水下水裂的实际解决方案.
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