Smart Detection System of Safety Hazards in Industry 5.0

Abstract

Safety management is a priority to guarantee human-centered manufacturing processes in the context of Industry 5.0, which aims to realize a safe human–machine environment based on knowledge-driven approaches. The traditional approaches for safety management in the industrial environment include staff training, regular inspections, warning signs, etc. Despite the fact that proactive measures and procedures have exceptional importance in the prevention of safety hazards, human–machine–environment coupling requires more sophisticated approaches able to provide automated, reliable, real-time, cost-effective, and adaptive hazard identification in complex manufacturing processes. In this context, the use of virtual reality (VR) can be exploited not only as a means of human training but also as part of the methodology to generate synthetic datasets for training AI models. In this paper, we propose a flexible and adjustable detection system that aims to enhance safety management in Industry 5.0 manufacturing through real-time monitoring and identification of hazards. The first stage of the system contains the synthetic data generation methodology, aiming to create a synthetic dataset via VR, while the second one concerns the training of AI object detectors for real-time inference. The methodology is evaluated by comparing the performance of models trained on both real-world data from a publicly available dataset and our generated synthetic data. Additionally, through a series of experiments, the optimal ratio of synthetic and real-world images is determined for training the object detector. It has been observed that even with a small amount of real-world data, training a robust AI model is achievable. Finally, we use the proposed methodology to generate a synthetic dataset of four classes as well as to train an AI algorithm for real-time detection.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges

“…A more thorough evaluation could be based on either a synthetic [ 75 , 76 ] or a real ground truth dataset. For the latter, the goal would be to capture several synchronized 3D scans and image frames of a slowly moving vehicle.…”

An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0

“…On the other hand, smart sensors and detection systems, as well as the wearable devices, are administrative controls offering risk awareness to the operators. Coupled with ML algorithms and being part of the IoT, they can be used as surveillance tools to monitor work patterns and give instructions [61]. Additional training tools are the Following the guidelines of a hierarchical hazard control-as also outlined in the ISO 45001 for the design of a health and safety management system-the tools of Industry 4.0 can be adopted in almost all aspects of hazard control and risk prevention.…”

“…On the other hand, smart sensors and detection systems, as well as the wearable devices, are administrative controls offering risk awareness to the operators. Coupled with ML algorithms and being part of the IoT, they can be used as surveillance tools to monitor work patterns and give instructions [61]. Additional training tools are the VR/AR for safety coordination, induction training and providing instructions in the workplace.…”