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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 6th 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 6th 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. [ABSTRACT FROM AUTHOR]

This paper presents a multi-modal recognition system (MMR) which can eliminates the need for using Barcodes and Radio Frequency Identification Systems (RFIDs). Barcodes and RFID have limitations; for example, Barcodes require the scanner to have a direct line of sight with the code, and they are more susceptible to errors. Barcodes can also be hard to locate and can be affixed to oddly shaped products. While, RFID may overcome such problems, but it could be disturbed if the RFID is attached to a metallic background. The proposed MMR system can monitor items flowing one by one down on a conveyor belt to make sure they match their images, thus enabling robotics identification for items while picking them up, sorting them or turning them into a desirable orientation. Recognizing the business landscape evolves and competition from low-cost nations grows, new models must be created that provide a competitive edge by combining the Lean paradigm with Industry 4.0 technical advancements. This paper reports a contribution to this field by assessing the supporting function of MMR state-of-the-art algorithms in Lean manufacturing. In addition, this paper also aims to explore how MMR could be integrated into the Lean manufacturing settings to enable a competitive manufacturing process in a Lean 4.0 environment. A dataset with 21,000 vegetable images of 15 classes was used to present the recent development and application of image analysis and computer vision systems in object recognition, showing an overall detection F1-score of 85.08%. [ABSTRACT FROM AUTHOR]