Surface-Type Classification Using RGB-D

This paper proposes an approach to improve surface-type classification of images containing inconsistently illuminated surfaces. When a mobile inspection robot is visually inspecting surface-types in a dark environment and a directional light source is used to illuminate the surfaces, the images captured may exhibit illumination variance that can be caused by the orientation and distance of the light source relative to the surfaces. In order to accurately classify the surface-types in these images, either the training image dataset needs to completely incorporate the illumination variance or a way to extract color features that can provide high classification accuracy needs to be identified. In this paper diffused reflectance values are extracted as new color features to classifying surface-types. In this approach, Red, Green, Blue-Depth (RGB-D) data is collected from the environment, and a reflectance model is used to calculate a diffused reflectance value for a pixel in each Red, Green, Blue (RGB) color channel. The diffused reflectance values can be used to train a multiclass support vector machine classifier to classify surface-types. Experiments are conducted in a mock bridge maintenance environment using a portable RGB-Depth sensor package with an attached light source to collect surface-type data. The performance of a classifier trained with diffused reflectance values is compared against classifiers trained with other color features including RGB and L*a*b* color spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and L*a*b* features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in o- erall accuracy over RGB and L*a*b* by 49.24% and 13.66%, respectively.