# Smart Detection System of Safety Hazards in Industry 5.0
## 2 Industry 5.0: Building a Human-Centric Industry
### Employee Safety in Industry 5.0 Manufacturing via Safety Management  
In human-centered Industry 5.0, ensuring employee safety in manufacturing is of high importance. The manufacturing sector has historically been characterized by elevated injury rates stemming from the complexities and risks associated with its operations. Specifically, in 2020, the EU reported that manufacturing had the highest number of non-fatal accidents (18.2% of the total) and was the sector with the second-highest number of fatal accidents (14.6% of the total) [11]. By establishing protective measures, employees can concentrate on their tasks rather than being preoccupied with potential risks and hazards in their environment. Studies indicate that employees who perceive their workplace as secure and safe tend to exhibit enhanced performance compared to those who feel insecure [12; 13].  
Since Industry 5.0 places humans at the center of the new-generation manufacturing system, emphasis should be given to human-machine-environmental safety based on knowledge-driven approaches. One fundamental component to ensure human-centered manufacturing towards Industry 5.0 is the establishment of intelligent factory safety management [14]. Conventional approaches to safety management involve employee training, routine inspections, the utilization of warning signs, etc. Those preventive measures hold significant importance for proactively mitigating safety hazards and risks. However, those traditional measures ignore the complexity of human-machine-environment coupling, which requires more sophisticated approaches able to provide automated, reliable, real-time, and cost-effective safety management methods.  
In the context of Industry 5.0, advanced technologies can be utilized to create intelligent factory safety management. This can be achieved by establishing an efficient monitoring mechanism to identify potential hazards in manufacturing. The system could comprise cameras strategically positioned in the space to capture real-time footage, which is then processed by object detection algorithms trained specifically on application-related data.  
Real-time detection can trigger alerts to relevant personnel when non-compliance events occur. The robustness and efficiency of the monitoring system heavily depend on the available data for object detection training.  
However, the complexity of the interaction between humans, machines, and the environment imposes challenges in formulating robust safety management strategies. A significant challenge lies in the adaptability of these safety methods; while one approach can be suitable for a particular case, it might not be relevant or applicable to another [15]. Therefore, it is crucial to develop safety management approaches that can be adaptable for exploitation in various manufacturing environments. This can be achieved by generating data tailored to specific applications and scenarios.  
Flexible and adaptable safety management for Industry 5.0 manufacturing can be designed by employing our proposed system. Initially, high-quality synthetic datasets can be generated via virtual environments (VEs) by exploiting the proposed synthetic data generation methodology. These data are artificially produced instead of being acquired from real-world events, which offers numerous notable advantages, including the possibility for adaptability across diverse environments. The methodology of this paper provides the steps to create synthetic datasets that can simulate a wide range of conditions, variables, scenarios, and settings, which becomes particularly valuable in environments where a large amount of real-world data acquisition is challenging or costly. Afterward, AI object detectors can be trained on synthetic datasets and utilized for real-time monitoring based on data streams from cameras. Additionally, innovative architecture optimizes network and service performance [16]. For the networking of the monitoring system, the capabilities of Virtual Network Embedding (VNE) can also be explored [17].