Learning monocular depth estimation for defect measurement from civil RGB-D dataset [0.03%]
基于民用RGB-D数据集的学习单目深度估计的缺陷测量方法
Max Midwinter,Zaid Abbas Al-Sabbag,Rishabh Bajaj et al.
Max Midwinter et al.
A large quantity of civil infrastructure in North America is near the end of their design life. Consequently, the routine visual structural inspection is increasingly necessary to ensure the safety and efficient management of the infrastruc...
Advanced deep learning framework for underwater object detection with multibeam forward-looking sonar [0.03%]
基于多波束前视声纳的水下目标检测深度学习框架
Liangfu Ge,Premjeet Singh,Ayan Sadhu
Liangfu Ge
Underwater object detection (UOD) is an essential activity in maintaining and monitoring underwater infrastructure, playing an important role in their efficient and low-risk asset management. In underwater environments, sonar, recognized fo...
Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring [0.03%]
基于深度学习的结构健康监测障碍物回避自主无人机及基于标志点的位置修正
Ali Waqas,Dongho Kang,Young-Jin Cha
Ali Waqas
This paper proposes a framework for obstacle-avoiding autonomous unmanned aerial vehicle (UAV) systems with a new obstacle avoidance method (OAM) and localization method for autonomous UAVs for structural health monitoring (SHM) in GPS-deni...
Deep learning-based concrete defects classification and detection using semantic segmentation [0.03%]
基于深度学习的语义分割混凝土缺陷分类与检测方法研究
Palisa Arafin,Ahm Muntasir Billah,Anas Issa
Palisa Arafin
Visual damage detection of infrastructure using deep learning (DL)-based computational approaches can facilitate a potential solution to reduce subjectivity yet increase the accuracy of the damage diagnoses and accessibility in a structural...
Efficient attention-based deep encoder and decoder for automatic crack segmentation [0.03%]
一种有效的注意力机制深度编码器和解码器的自动裂缝分割方法
Dong H Kang,Young-Jin Cha
Dong H Kang
Recently, crack segmentation studies have been investigated using deep convolutional neural networks. However, significant deficiencies remain in the preparation of ground truth data, consideration of complex scenes, development of an objec...