# Machine Vision--Moving from Industry 4.0 to Industry 5.0
## 2 Foundations
### Industry 5.0  
While one would expect that the end of one era would bring the beginning of another, this is not the case with the era of Industry 5.0. Already, within ten years of the advent of Industry 4.0, the European Commission brought to the fore and talked about Industry 5.0, outlining a series of implementation strategies from investment, marketing, and governance dimensions to promote it [46]. Until now, the response from other governments and industries is still limited. However, academia quickly embraced Industry 5.0; the Journal of Manufacturing Systems, the International Journal of Production Research, and the IEEE Transactions on Industrial Informatics established related special topics to encourage Industry 5.0 research in 2021. The Technical Committee (TC) on Digital Manufacturing and Human-Centered Automation also highlighted its relevance to Industry 5.0 [47]. Future technologies of Industry 5.0 (Figure 3) are summarized in the following:  
* Personalized human-machine interaction technologies that interconnect and combine the advantages of both humans and machines. * Bio-inspired technologies and smart materials, which enable recyclable materials with built-in sensors and improved features. * Digital twins and simulation to achieve modelling of entire systems. * Technologies related to transmission, storage, and analysis of data, with data processing and system interoperability. * Artificial intelligence to detect losses in complex dynamic systems, leading to actionable insights. * Environmentally friendly technologies (energy efficiency, renewable energy sources, storage, and autonomy). Based on the above, it appears that the advent of Industry 5.0 is not just a technology-driven revolution, but is driven by values that will lead to purposeful technological transformation [48]. In Industry 5.0, operators and robots come together and collaborate to implement complex projects in a variety of scenarios with heterogeneous and dynamic conditions. Humans can work interactively with robots, by assigning them to repetitive or dangerous tasks. Less physical work and more safety are therefore guaranteed for human workers [49]. In the development of these models, a decisive and unique role is played by machine vision as a means of sensing, providing reception and processing of visual information about the environment, analysis of images of the workplace, transfer of relevant information to the control center, and decision making. In this way, the task of recognizing the actions of a human operator, in order to apply the extracted knowledge towards an effective human-robot cooperation system, becomes important [50]. A typical example of the above collaboration between humans and robots is provided by [PERSON] et al. [51] for an Industry 5.0 application of an end-to-end edge system based on machine vision. More specifically, a machine vision system is presented, which relies on cloud services for data storage and training of AI-based models, for the application of bin collection in industrial facilities. The system is intended to work with Universal Robots Collaborative Robots, namely cobots. In industry, bin collection refers to picking and moving objects (bins) to predetermined locations within the cobot's work area. In the case study of [PERSON] et al. [51], the robot is constantly encountering objects it has never seen before; thus, it needs to learn them. The new objects are displayed to the robot by the human operator, posing an incremental learning challenge, towards automatically labeling the new training data to avoid time-consuming and expensive labeling processes. Typical development of an object detection system involves training a model on a dataset in the cloud. After the model is successfully trained, it is used on an edge device in the production line. However, there are several cases where the system should learn new objects in the production line, without forgetting the previous ones [52]. [PERSON] et al. [52] in their study used 100 photos to train the model and 25 photos for each old object.