Deep-Learning-Based Anti-Collision System for Construction Equipment Operators

Yun-Sung Lee (), Do-Keun Kim and Jung-Hoon Kim
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Yun-Sung Lee: Smart Construction Promotion Center, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea
Do-Keun Kim: Research and Development Center, Youngshine, Hanam-si 12939, Republic of Korea
Jung-Hoon Kim: Eco Smart Solution Team, SK Ecoplant, Jongno-gu, Seoul 03143, Republic of Korea

Sustainability, 2023, vol. 15, issue 23, 1-28

Abstract: Due to the dynamic environment of construction sites, worker collisions and stray accidents caused by heavy equipment are constantly occurring. In this study, a deep learning-based anti-collision system was developed to improve the existing proximity warning systems and to monitor the surroundings in real time. The technology proposed in this paper consists of an AI monitor, an image collection camera, and an alarm device. The AI monitor has a built-in object detection algorithm, automatically detects the operator from the image input from the camera, and notifies the operator of a danger warning. The deep learning-based object detection algorithm was trained with an image data set composed of a total of 42,620 newly constructed in this study. The proposed technology was installed on an excavator, which is the main equipment operated at the construction site, and performance tests were performed, and it showed the potential to effectively prevent collision accidents.

Keywords: construction; deep learning; anti-collision system; AI monitor; object detection algorithm (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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