## 1 Introduction  
The current production paradigm, characterized by mass customization, entails an increased risk of non-compliance within production systems. The growing complexity of products and the shortening of their life cycles, in fact, lead to a higher likelihood of defects occurring during production [1]. Non-compliant products pose a serious threat to manufacturers across all sectors. Internally, a defect can incur costly rework in operational expenses, resource allocation, and delivery delays [2, 3], or even result in scrapping. Thus, the negative impact is not solely economic but also affects the sustainability of production processes. Externally, managing the delivery of non-conforming products to customers generates substantial costs and can damage the brand's reputation [4]. Consequently, manufacturers are compelled to deploy quality assurance strategies to mitigate these risks. As noted by [PERSON] [5], quality assurance comprises the activities that ensure the maintenance of product and service quality levels and the proper resolution of supplier and customer quality issues. One of the key solutions in this domain is inspection, defined by ISO 2859-1 [6] as any activity--such as measuring, examining, testing or gauging one or more product characteristics--and comparing the results with specified requirements to determine whether each characteristic conforms. Particularly promising are non-destructive inspections, which enable the measurement of certain critical product factors without compromising their integrity [7]. For example, visual inspections rely exclusively on observation, without physical contact with the products [8]. Traditionally conducted by human operators, these inspections are essential for upholding quality standards [9; 10]; however, the performance of human inspectors may vary due to fatigue, distraction, or inconsistent judgment [11]. This variability can lead to inconsistent inspection results and potentially overlooked non-conformities. In the modern era of Industry 5.0, technological solutions have increasingly been introduced to support human operators and enhance the overall quality performance of inspection systems [12; 13]. Indeed, recent advancements in artificial intelligence have shown significant potential to enhance manufacturing and supply chain operations across various domains [14; 15], with quality control emerging as a particularly promising application area. Fully automated visual inspection systems have been developed to completely replace human activities, relieving operators from physically demanding inspections and providing superior defect detection capabilities. Among fully automated systems, two main categories can be distinguished: semi-fixed solutions, where images are collected by cameras mounted in fixed positions or moving along predefined tracks [16; 17; 18; 19], and more flexible, albeit more costly, solutions employing robotic or collaborative robotic (cobot) systems for image acquisition [20; 21; 22; 23]. Between the two extremes--completely automated systems and purely human-based inspections--hybrid systems have emerged. In these configurations, image acquisition is performed manually by operators using hand-held or head-mounted devices, while image processing and defect detection are automated through CNNs [24; 25; 26; 27]. Hybrid approaches combine high flexibility and effective defect detection with lower investment costs compared to fully automated systems. However, they typically entail higher operational costs due to the need for human intervention and longer acquisition times. It is important to note that these inspection methods are not inherently mutually exclusive. Technological solutions often complement, rather than entirely replace, human activities. This is particularly evident in the field of Human-Robot Collaboration (HRC), where synergistic interactions between humans and machines are actively studied [12; 28; 29]. Visual inspections thus represent a vital tool for manufacturers in ensuring high production quality.