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Article

Development and Application of Small Object Visual Recognition Algorithm in Assisting Safety Management of Tower Cranes

by
Xiao Sun
1,*,
Xueying Lu
1,
Yao Wang
2,
Tianxiao He
1 and
Zhenghong Tian
1
1
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
2
China Railway First Survey and Design Institute Group Co., Ltd., Xiying Road No. 2, Xi’an 710043, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(12), 3728; https://doi.org/10.3390/buildings14123728
Submission received: 10 October 2024 / Revised: 17 November 2024 / Accepted: 19 November 2024 / Published: 23 November 2024
(This article belongs to the Section Construction Management, and Computers & Digitization)

Abstract

This study presents a novel video-based risk assessment and safety management technique aimed at mitigating the risk of falling objects during tower crane lifting operations. The conventional YOLOv5 algorithm is prone to issues of missed and false detections, particularly when identifying small objects. To address these limitations, the algorithm is enhanced by incorporating an additional small object detection layer, implementing an attention mechanism, and modifying the loss function. The enhanced YOLOv5s model achieved precision and recall rates of 96.00%, with average precision (AP) values of 96.42% at an IoU of 0.5 and 62.02% across the range of IoU values from 0.5 to 0.95. These improvements significantly enhance the model’s capability to accurately detect crane hooks and personnel. Upon identifying the hook within a video frame, its actual height is calculated using an interpolation function derived from the hook’s dimensions. This calculation allows for the precise demarcation of the danger zone by determining the potential impact area of falling objects. The worker’s risk level is assessed using a refined method based on the statistical analysis of past accidents. If the risk level surpasses a predetermined safety threshold, the worker’s detection box is emphasized and flagged as a caution on the monitoring display.
Keywords: tower crane falling object striking; small object detection; visual recognition; risk assessment; safety management tower crane falling object striking; small object detection; visual recognition; risk assessment; safety management

Share and Cite

MDPI and ACS Style

Sun, X.; Lu, X.; Wang, Y.; He, T.; Tian, Z. Development and Application of Small Object Visual Recognition Algorithm in Assisting Safety Management of Tower Cranes. Buildings 2024, 14, 3728. https://doi.org/10.3390/buildings14123728

AMA Style

Sun X, Lu X, Wang Y, He T, Tian Z. Development and Application of Small Object Visual Recognition Algorithm in Assisting Safety Management of Tower Cranes. Buildings. 2024; 14(12):3728. https://doi.org/10.3390/buildings14123728

Chicago/Turabian Style

Sun, Xiao, Xueying Lu, Yao Wang, Tianxiao He, and Zhenghong Tian. 2024. "Development and Application of Small Object Visual Recognition Algorithm in Assisting Safety Management of Tower Cranes" Buildings 14, no. 12: 3728. https://doi.org/10.3390/buildings14123728

APA Style

Sun, X., Lu, X., Wang, Y., He, T., & Tian, Z. (2024). Development and Application of Small Object Visual Recognition Algorithm in Assisting Safety Management of Tower Cranes. Buildings, 14(12), 3728. https://doi.org/10.3390/buildings14123728

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