Enhanced safety implementation in 5S + 1 via object detection algorithms

• Published in: International journal of advanced manufacturing technology 2023-04, Vol.125 (7-8), p.3701-3721
• Main Authors: Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, Khodadadi Koodiani, Hamid, Bouzary, Hamed, Shahin, Awni
• Format: Article
• Language: English
• Subjects: Algorithms; CAE) and Design; Compliance; Computer-Aided Engineering (CAD; Deep learning; Engineering; Frames per second; Industrial and Production Engineering; Industrial safety; Machine learning; Manufacturing; Mechanical Engineering; Media Management; Object recognition; Occupational hazards; Original Article; Performance measurement; Personal protective equipment; Safety helmets; Workers
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-10970-9

Scholarly work points to 5S + 1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6 th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6 th S in 5S + 1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing personal protective equipment (PPE). The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers’ PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and verifies PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 frames per second (FPS), making it suitable for real-time detection.