# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 3 Methodology
### Real-Time Processing  
The real-time processing stage of the MR-based quality control system involves creating an architectural design for analyzing and processing the video stream captured by smart glasses. The architecture can be conceptually divided into two main segments: the client-side MR system and the server-side image recognition system. The client-side MR system utilizes a smart glasses device, which the quality inspector interacts with. This MR headset captures the video stream of the real-world environment, primarily components inspected for quality control, and streams it to the server. Equipped  
Figure 2: Proposed framework for model training. with depth sensors and multiple cameras, the device captures a rich, three-dimensional view crucial for accurately overlaying augmented reality elements on the real-world scene. On the server side, the system analyzes the incoming video stream, identifying objects and detecting defects. The server runs a machine learning model and utilizes a framework for real-time video processing and computer vision tasks. Combining an MR headset with a powerful server-based image recognition system, this architecture enables a robust and efficient MR-based quality control system that surpasses traditional manual inspection in speed and accuracy. Figure 3 illustrates the proposed framework for real-time processing and visualization within a smart glasses environment for quality inspection. The process begins with natural objects on the assembly line, serving as subjects for quality inspection. The smart glasses act as intermediaries, capturing images from the scene and live videos of the assembly process. They not only capture the feed but also serve as the display for visual output, allowing the user to see results superimposed on the real-world view. In this methodology, OpenCV, an open-source computer vision library, assumes a critical role as the initial gateway for processing the incoming video stream. Its multifaceted capabilities are harnessed to undertake a range of image-processing tasks vital for the preliminary assessment of the assembly process. As the continuous video stream is received, OpenCV takes charge, parsing it into discrete frames and thereby laying the foundation for meticulous frame-by-frame analysis. Once the frames are extracted, OpenCV seamlessly integrates with the pre-trained part identification model to conduct a granular examination of the individual components and parts within each frame. The versatility of OpenCV shines in its ability to handle diverse image processing operations, ensuring an accurate identification of components even in complex visual scenarios. This preliminary analysis sets the stage for the subsequent phases of the methodology. Following OpenCV's comprehensive pre-processing, the torch is passed to the YOLOv8 object detection system, which comes into play as the primary engine for identifying and discerning between different components. YOLOv8, having been pre-trained on an extensive dataset specific to the assembly process under investigation, employs its neural network architecture to execute rapid and precise object detection. Its proficiency lies in distinguishing correct assemblies from incorrect ones with exceptional accuracy and speed, which is a characteristic vital for real-time analysis. OpenCV is chosen for its versatility, high-performance features, and robustness, making it an ideal tool for analyzing the assembly process in this methodology. Its comprehensive suite of image-processing functions facilitates the parsing of continuous video streams into individual frames, allowing for diverse operations such as filtering,  
Figure 3: Proposed framework for real-time processing. transformation, and enhancement. The capability for frame-by-frame analysis is particularly valuable in scenarios requiring a detailed inspection, as commonly encountered in assembly processes [53; 54; 55]. The interaction between OpenCV and YOLOv8 is synergistic.