Application of YOLO v5 and v8 for Recognition of Safety Risk Factors at Construction Sites

Kyunghwan Kim (), Kangeun Kim and Soyoon Jeong
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Kyunghwan Kim: Department of Architecture, Konkuk University, Seoul 05029, Republic of Korea
Kangeun Kim: Department of Architecture, Konkuk University, Seoul 05029, Republic of Korea
Soyoon Jeong: DL E&C, Seoul 03181, Republic of Korea

Sustainability, 2023, vol. 15, issue 20, 1-17

Abstract: The construction industry has high accident and fatality rates owing to time and cost pressures as well as hazardous working environments caused by heavy construction equipment and temporary structures. Thus, safety management at construction sites is essential, and extensive investments are made in management and technology to reduce accidents. This study aims to improve the accuracy of object recognition and classification that is the foundation of the automatic detection of safety risk factors at construction sites, using YOLO v5, which has been acknowledged in several studies for its high performance, and the recently released YOLO v8. Images were collected through web crawling and labeled into three classes to form the dataset. Based on this dataset, accuracy was improved by changing epochs, optimizers, and hyperparameter conditions. In each YOLO version, the highest accuracy is achieved by the extra-large model, with mAP50 test accuracies of 94.1% in v5 and 95.1% in v8. This study could be further expanded for application in various management tools at construction sites to improve the work process, quality control, and progress management in addition to safety management through the collection of more image data and automation for accuracy improvement.

Keywords: safety; construction industry; computer vision; object detection; YOLO v5; YOLO v8 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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