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The accumulative freeze-thaw cycles in cold regions could cause deteriorations to concrete pavements. The measurement of air-void system inside hardened concrete is important for estimating the freeze-thawing performance of a concrete pavement. The segmentation of air voids from the concrete surface image is one of the most essential and challenging steps for automated air-void measurement. Conventional automated air-voids segmentation methods rely on a color enhancement step to manually create a contrast between air voids and non-air-voids. This study aims to investigate the use of a photometric stereo method for automated segmentation of air voids in hardened concrete surface without a need for contrast enhancement. In the study, the photometric stereo system with a Charge-Coupled Device (CCD) camera and six illumination lights was designed to collect images of polished concrete specimen surfaces under various lighting directions. Two air-void segmentation approaches were developed in the study. One approach interpreted the air voids by the surface depth. The other approach interpreted the air voids by the surface normal. Consequently, the segmentation results were refined by a set of image processing procedures. The results showed that the proposed automated air-void segmentation procedures could detect air voids in concrete surface with a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

• Automated segmentation of air voids from hardened concrete surface. • Three-dimensional image reconstruction method. • Deep learning strategies and process. The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor intensive. This study investigated the utilization of three-dimensional (3D) reconstruction and Deep Convolution Neural Network (DCNN) methods to detect the air voids in hardened concrete surfaces without the use of contrast enhancement. The experimental results showed that the DCNN could accurately distinguish air voids from hardened concrete images with the detection accuracy of over 0.9 in only less than a minute. The accuracy rates for air content, specific surface, and spacing factor were 0.92, 0.91, and 0.89, respectively. [ABSTRACT FROM AUTHOR]