## 5 Conclusions  
To standardize the concrete crack image capture scene and produce a high-quality concrete crack image dataset, this paper presents exposure as the scene characterizing parameter for concrete crack image capture based on a standardization investigation, the analysis of current public concrete crack segmentation dataset, and a photoelectric principal analysis. Through equivalent exposure scene design and validation, the optimal exposure interval is proposed in 50 scenes. The main conclusions of this paper are drawn as follows:  
(1) The analysis of present publicly accessible datasets showed that non-uniform image capture devices, spatial resolution, image size and number, mislabeling, inappropriate spatial resolution or unnecessary backgrounds, sourced images, out-of-focus images, and motion blur are frequent problems. Thus, the standardization of concrete crack image acquisition scenes is a great challenge for high-precision concrete detection. Figure 14: Segmentation of three specimens in different equivalent exposure scenes. Figure 13: F1 score of three specimens under different exposure scenes. (2) Based on the photoelectric principle of the concrete crack imaging process, equivalent exposure was taken as the scene characterization parameter for machine-vision-based infrastructure crack detection. Twenty-one equivalent exposure scenes were designed, and the law of image histograms, mean values, and standard deviations were analyzed to validate the effectiveness of equivalent exposure or the equivalence of exposure time control and illumination control in crack detection. (3) Concrete crack segmentation of images from 50 equivalent exposure scenes revealed that the highest segmentation precision happened within the 5-50 lx-s equivalent exposure interval, and the F1 score could reach 96.3%. In addition, high exposure was detrimental to concrete crack detection. This paper found that the standardization of the concrete crack image scene was significant. Optimal equivalent exposure was the core characterization index, which could help to enhance crack segmentation accuracy. The factors that constitute the concrete crack image scene were multiple and complex. In this paper, the experimental specimen number was limited, the illumination was uniform, and the validation was an experiment conducted indoors. More outdoor or onsite experiments should be carried out on more real infrastructure members in complex illumination environments. In addition, the comprehensive understanding of concrete crack imaging scene still needs further exploration. At the same time, the dataset quality evaluation method and characteristic index are not totally clear and should be further explored, and there are factors that influence the quality of datasets, like imaging spatial resolution and image size, which still need more investigation and validation with deep learning algorithms. H.L. : conceptualization, methodology, and writing--review and editing. R.L. : data collection, software, and writing--original draft and editing. Y.L. : experiment, data collection, and algorithm. J.H. : data collection and image labeling. Y.S. : data collection, analysis, and image labeling. J.Z. : conceptualization, formal analysis, and writing--review. Z.Z. : conceptualization and formal analysis. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant. No. 51778094), the Chongqing Municipal Education Commission (Grant. No. JDLHPYJD2020019, Grant. No. JDDSTD2022003), the Department of Transportation of Sichuan Province (Grant No. 2020-ZL-B1), and the Chongqing Municipal Education Commission (Grant. No. CYS22398). We also acknowledge the State Key Laboratory of Mountain Bridge and Tunnel Engineering for providing experimental equipment. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request.