Title: An efficient deep convolutional neural network-based safety monitoring system for construction sites

Authors: V. Ashwanth; Dhanya Sudarsan

Addresses: Department of Computer Science and Engineering, Muthoot Institute of Technology and Science, Kochi, Kerala, India ' Department of Computer Science and Engineering, Muthoot Institute of Technology and Science, Kochi, Kerala, India

Abstract: Worker safety and health are paramount concerns, especially in high-risk occupations such as construction works. Monitoring workers to ensure proper usage of personal protective equipment (PPE) at construction sites is essential. However, manual surveillance via CCTV footage is time-consuming. This paper proposes an automated approach for construction site monitoring without human intervention. Initially, YOLOv4 is employed for construction worker detection, with subsequent division of the bounding boxes into four halves. EfficientNet is then utilised to analyse these cropped sections and identify specific PPE components. Additionally, construction tools and equipment are recognised, and a safety score is assigned based on worker proximity to these objects. Unsafe workers are flagged as 'danger zone' in each frame, alongside the marking of workers. This approach streamlines safety monitoring processes while ensuring worker well-being.

Keywords: computer vision; YOLOv4; construction safety; EfficientNet-B5; transfer learning; PPE; custom labelling; safety detection; object detection; image classification; machine learning.

DOI: 10.1504/IJCVR.2025.144773

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.2, pp.152 - 176

Received: 31 May 2022
Accepted: 26 Jun 2023
Published online: 03 Mar 2025 *

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