一个实时移动机器人系统用于在施工中检测裂,使用双阶段深度学习
Emmanuella Ogun1, Yong Ann Voeurn2, Doyun Lee2
1Mechanical Engineering Department, Georgia Southern University, Statesboro, GA 30460, USA.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了用于基础设施检查的实时机器人系统,使用深度学习进行自动裂检测和自主导航. 该系统成功地识别了微裂,提高了公共安全和检查效率.
科学领域:
- 土木工程 土木工程是指土木工程.
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 民用基础设施的恶化带来了重大的公共安全风险.
- 手动检查是主观的,劳动密集型的,并且受到可访问性的限制.
- 需要自动化检查系统来克服这些局限性.
研究的目的:
- 开发和评估一个实时机器人检查系统,整合深度学习和自主导航.
- 为了使同时实现自动化裂检测和无碰撞导航,用于基础设施评估.
- 提高民用基础设施检查的效率,准确性和安全性.
主要方法:
- 一个两阶段的深度学习神经网络:U-Net用于细分和Pix2Pix条件生成对抗网络 (GAN)用于改进.
- 敌对的残留学习来提高边界准确性和减少错误的阳性.
- 在无人地面车辆 (UGV) 上部署,配备RGB-D摄像头和LiDAR用于感知和导航.
主要成果:
- 两阶段模型在CrackSeg9k数据集上实现了73.9 ± 0.6%的平均交叉在欧盟 (mIoU) 和76.4 ± 0.3%的F1得分.
- 在各种验证测试中,机器人系统成功地检测到小到0.3毫米的微裂.
- 在模拟,实验室实验和现实世界的校园走廊测试中表现出强大的性能.
结论:
- 拟议的机器人系统为现场部署的基础设施检查提供了强大的自主解决方案.
- 深度学习集成显著提高了自动裂检测能力.
- 该系统有可能彻底改变基础设施监控和维护实践.
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