Evaluation of RGB-D Image for Counting Exposed Aggregate Number on Pavement Surface Based on Computer Vision Technique

Abstract

Functional performance of Expose Aggregate Concrete Pavement (EACP) such low tire-pavement noise and higher skid resistance are noticeable due to long-term durability, are influenced by wavelength and mean texture depth (MTD). EACP surface macrotexture is characterized by the MTD and exposed aggregate number (EAN) due to a higher correlation between wavelength and the EAN. Normally, the EAN is manually estimated which needs much human effort and is time-consuming. Recently, deep learning of computer vision has been employed for aiding human counting tasks in different condition. Mostly, many state-of-the-arts for counting are conducted by using RGB image which is color image. Regarding the counting techniques used for EAN, it is a challenging task to deal with some issues such as aggregate is some occluded and similar coloring to the background. Because the aggregate shows the peak characteristic, the depth value may benefit in improving the recognition. This additional information may be useful since it can be display distinguishable color between the object and background. Therefore, this study aims to evaluate the combination of RGB image and depth information, knowns as RGB-D image, for counting the EAN by adapted Faster RCNN deep learning model with four channel input images. The RGB-D dataset was newly constructed for training and testing implemented model. The result shows the accuracy slightly improve by 5% by using RGB-D compared to RGB. However, they both achieve similar MAE and RMSE. Therefore, it gives the valuable information for EAN counting. Both image datasets are acceptable for counting the EAN with a given condition.