A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated Rust-Defect Detection of a Steel Bridge Using Aerial Multispectral Imagery
Computer vision methods have the potential to detect rust defects in steel components of bridges. However, direct use of images collected by aerial means to identify such defects is currently difficult because of obstructions caused by other objects in the image field of view. In this context, an automated rust-defect-determination method that leverages aerial imagery, including both visible and infrared images, is presented in this investigation. The proposed method consists of three steps. The first step deals with image registration for which a binary information method is proposed to match the infrared images to their visible counterparts. In the second step, bridge components are retrieved from the captured images via automated segmentation obtained by fusion of visible and infrared images. Finally, rusted regions are identified in YCbCr colorspace, and a rust percentage is calculated. Experimental results obtained by aerial images collected on a real operating structure demonstrate that the proposed methodology can directly use the original captured images and can be successfully applied to real-world scenarios.
Automated Rust-Defect Detection of a Steel Bridge Using Aerial Multispectral Imagery
Computer vision methods have the potential to detect rust defects in steel components of bridges. However, direct use of images collected by aerial means to identify such defects is currently difficult because of obstructions caused by other objects in the image field of view. In this context, an automated rust-defect-determination method that leverages aerial imagery, including both visible and infrared images, is presented in this investigation. The proposed method consists of three steps. The first step deals with image registration for which a binary information method is proposed to match the infrared images to their visible counterparts. In the second step, bridge components are retrieved from the captured images via automated segmentation obtained by fusion of visible and infrared images. Finally, rusted regions are identified in YCbCr colorspace, and a rust percentage is calculated. Experimental results obtained by aerial images collected on a real operating structure demonstrate that the proposed methodology can directly use the original captured images and can be successfully applied to real-world scenarios.
Automated Rust-Defect Detection of a Steel Bridge Using Aerial Multispectral Imagery
Li, Yundong (author) / Kontsos, Antonios (author) / Bartoli, Ivan (author)
2019-03-29
Article (Journal)
Electronic Resource
Unknown
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