In this study, a faster region-based convolutional neural network is constructed and applied to the combined vision and thermographic images for automated detection and classification of surface and subsurface corrosion in steel bridges. First, a hybrid imaging system is developed for the seamless integration of vision and infrared images. Herein, a three-dimensional red/green/blue vision image is obtained with a vision camera, and a one-dimensional active infrared (IR) amplitude image is obtained from the infrared camera for temperature measurements with halogen lamps as the heat source. Subsequently, the three-dimensional red/green/blue vision image is converted to a two-dimensional chroma blue- and red-difference (CbCr) image because the CbCr image is known to be more sensitive to surface corrosion than the red/green/blue image. A combined three-dimensional (CbCr-IR) image is then constructed by fusing the two-dimensional CbCr image and the one-dimensional infrared image. For the automated corrosion detection and classification, a faster region-based convolutional neural network is constructed and trained using the combined three-dimensional CbCr-IR images of surface and subsurface corrosion on steel bridge structures. Finally, the performance of the trained, faster region-based convolutional neural network is evaluated using the images acquired from real bridges and compared with faster region-based convolutional neural networks trained by other vision and IR-based images. The uniqueness of this study is attributed to the (1) corrosion detection reliability improvements based on the fusion of vision and infrared images, (2) automated corrosion detection and classification with a faster region-based convolutional neural network, (3) detection of subsurface corrosion that is not detectable using vision images only, and (4) application to field bridge inspection.
Compressive spectral imaging system for soil classification with three-dimensional convolutional neural network