End-to-end concrete appearance analysis based on pixel-wise semantic segmentation and CIE Lab

The traditional expert-on-site acceptance method has been unable to meet the need of intellectualization of modern concrete industry. The cutting-edge achievements in the field of computer vision have immensely accelerated the automation of concrete surface-related engineering. We propose an end-to-end concrete appearance multiple identification method based on pixel-wise semantic segmentation and a corresponding method for quantitative chromatic aberration analysis based on CIE Lab. The objective function of the proposed Fully Convolutional Network (FCN)-based network was optimized in terms of both empirical and structural risks, and the complexity was reduced to improve its generalization performance. After comparing different preposed base CNNs, the best results show 98.43 % pixel accuracy (PA), 91.33 % mean pixel accuracy (MPA), 83.89 % mean intersection over union (MIoU), and 96.95 % frequency weighted intersection over union (FWIoU). In addition, intra-class variation was explored under different light intensity and surface moisture content to demonstrate the robustness of the proposed method.