While structural displacement provides essential information about static and/or low-frequency dynamic characteristics of structural behaviors, full-scale measurement of absolute displacement in field structures is extremely challenging because of the requirement of fixed reference in most cases. Recent computer vision-based sensing technologies have advanced to the level of reference-free monitoring of full-scale dynamic displacement using generic features of the structure. However, current generic feature-based methods have limited to only short-term or campaign-type monitoring applications due to the intrinsic limitations of computer-vision sensing under variable environmental conditions. This study investigates deep learning-based approaches for real-time computer-vision sensing that enables displacement monitoring using generic features under harsh environmental uncertainties. Distractor-Aware Siamese Region Proposal Network (DaSiamRPN) was employed to address the environmental uncertainty issues, particularly caused by luminous condition change and obstructed vision, without sacrificing real-time processing capability. A series of indoor and outdoor experiments have been conducted to evaluate the performance under light condition change, occlusion, and haze. Comparative tests showed that the proposed method outperformed other various vision-based object tracking methods, showing the feasibility for long-term structural displacement monitoring of full-scale structures.

中文翻译：

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基于深度学习的环境不确定性结构位移监测的实时通用目标跟踪

虽然结构位移提供了有关结构行为的静态和/或低频动态特性的基本信息，但由于在大多数情况下需要固定参考，因此现场结构中绝对位移的全尺寸测量极具挑战性。最近基于计算机视觉的传感技术已经发展到使用结构的通用特征对全尺寸动态位移进行无参考监测的水平。然而，由于计算机视觉传感在可变环境条件下的内在限制，当前基于特征的通用方法仅限于短期或活动类型的监测应用。本研究研究了基于深度学习的实时计算机视觉传感方法，该方法能够在恶劣的环境不确定性下使用通用特征进行位移监测。Distractor-Aware Siamese Region Proposal Network (DaSiamRPN) 用于解决环境不确定性问题，特别是由照明条件变化和视力障碍引起的问题，同时不牺牲实时处理能力。已经进行了一系列室内和室外实验，以评估在光照条件变化、遮挡和雾霾下的性能。对比测试表明，该方法优于其他各种基于视觉的目标跟踪方法，显示了对全尺寸结构进行长期结构位移监测的可行性。Distractor-Aware Siamese Region Proposal Network (DaSiamRPN) 用于解决环境不确定性问题，特别是由照明条件变化和视力障碍引起的问题，同时不牺牲实时处理能力。已经进行了一系列室内和室外实验，以评估在光照条件变化、遮挡和雾霾下的性能。对比测试表明，该方法优于其他各种基于视觉的目标跟踪方法，显示了对全尺寸结构进行长期结构位移监测的可行性。Distractor-Aware Siamese Region Proposal Network (DaSiamRPN) 用于解决环境不确定性问题，特别是由照明条件变化和视力障碍引起的问题，同时不牺牲实时处理能力。已经进行了一系列室内和室外实验，以评估在光照条件变化、遮挡和雾霾下的性能。对比测试表明，该方法优于其他各种基于视觉的目标跟踪方法，显示了对全尺寸结构进行长期结构位移监测的可行性。已经进行了一系列室内和室外实验，以评估在光照条件变化、遮挡和雾霾下的性能。对比测试表明，该方法优于其他各种基于视觉的目标跟踪方法，显示了对全尺寸结构进行长期结构位移监测的可行性。已经进行了一系列室内和室外实验，以评估在光照条件变化、遮挡和雾霾下的性能。对比测试表明，该方法优于其他各种基于视觉的目标跟踪方法，显示了对全尺寸结构进行长期结构位移监测的可行性。