# Image Processing and QR Code Application Method for Construction Safety Management
## 5 Conclusions  
This study deals with a study on developing a methodology for construction safety management automation using surveillance cameras installed on site. In this paper, three methodologies were presented: (1) classification and recognition of object; (2) allocating unique identification information using QR code; (3) extracting an object's coordinates projecting onto a 2D plane. During the recruitment process at the experiment site, the research team was able to conduct the test on the condition that it does not infringe on the work environment of the on-site staff. Accordingly, there is a limitation in that workers and managers did not conduct the surveys. The authors discussed the case study results with safety managers. Managers have commented that this methodology is easy to apply on-site because it does not require additional equipment installation and does not require special instructions to workers. Moreover, there was an opinion that immediate application could positively affect major management targets (e.g., less than 1 year of experience, workers in hazardous areas).  
The images that are using for training were taken under sufficient brightness. Because these images easily distinguish between objects and backgrounds, neural networks trained with data can record high object identification rates for images with vivid colors. However, most of the construction task is done outside, and the amount of sunlight fluctuates depending on the location of the clouds and the sun. Besides, images shot in the shade become very difficult to distinguish between the object and the background. This situation increases the uncertainty and inaccuracy of object recognition. In the study of  
Figure 9: Applying process and results of case study: (**a**) location of the camera installed on-site and 4 points to be used as the location standard; (**b**) on-site video file; (**c**) polygon plane created from 4 points (T1–T4) set in the image; (**d**) QR code drawn on clothes with a stencil technique to identify each worker’s ID; (**e**) identification result of each object and reading result of QR code information; (**f**) estimating the location of the operator “00121” and marking the location on the map.  
[PERSON] et al. [41], an approach through QR code was proposed so that workers could easily access and understand information on the precautions during work and obtained meaningful results that enable workers to prepare themselves for injuries. In addition to these results, to feedback personalized statistical data to each worker, it will be possible to improve the accident prevention effect by considering the physical fatigue and injuries caused by the worker's improper posture.  
Accordingly, in future research, it is necessary to improve object recognition accuracy by applying fuzzy theory [42; 43] and to obtain personal safety information by applying improper working posture recognition [44; 45].  
This study provides the basis for a methodology that can automatically collect each worker's individual safety history. If the problems described above are solved, and the system's reliability is sufficiently secured, it is expected that even the simple process of data updating [46] by a manager using a smart device can be omitted.  
Conceptualization, Y.-J.P. and C.-Y.Y.; methodology, J.-S.K., C.-Y.Y. and Y.-J.P.; software, J.-S.K.; validation, C.-Y.Y., J.-S.K. and Y.-J.P.; formal analysis, C.-Y.Y. and Y.-J.P.; investigation, J.-S.K., C.-Y.Y. and Y.-J.P.; resources, Y.-J.P.; data curation, J.-S.K.; writing--original draft preparation, J.-S.K., C.-Y.Y. and Y.-J.P.; writing--review and editing, Y.-J.P.; visualization, J.-S.K.; supervision, Y.-J.P.; project administration, Y.-J.P.; funding acquisition, Y.-J.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Kyungpook National University Development Project Research Fund, 2018.  
Not applicable.  
The authors declare no conflict of interest