Deep learning based image reconstruction at any speeds for faster pavement texture measurement using 3D laser technology

Abstract

ABSTRACT Recently, the super-resolution network PT-SRGAN was developed for faster pavement texture measurement using 3D laser technology at 0.1 mm resolution from only six predetermined faster speeds. This paper further introduces an extended application of PT-SRGAN in combination with bicubic interpolation to enhance the low-resolution 3D pavement data collected at any speeds for more flexible practices. The research team collected two datasets on ten field pavement sections: (1) Dataset-1 was the true 0.1 mm 3D texture data collected at very low speeds (<1.5 mph); and (2) Dataset-2 was the low-resolution 3D texture data collected at three faster speeds (7.5, 15, and 30 mph). The efficacy of the extended PT-SRGAN was validated by reconstructing 0.1 mm data from manually downsized low-resolution Dataset-1 and real low-resolution Dataset-2. First, the superior performance of the proposed method was demonstrated by examining two evaluation metrics calculated between ground truth and reconstructed images at different speeds using Dataset-1. Further, seven 3D areal texture parameters were calculated and averaged for reconstructed Dataset-2, and compared with those of Dataset-1 to demonstrate that the proposed method shows promising performance to enhance real low-resolution 3D texture images to 0.1 mm data for any faster pavement texture evaluation.