# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 4 Results
### Dataset  
The initial phase of model training for the quality control system relies on gathering images from the pneumatic cylinder prototype. The quality and diversity of this image dataset form the bedrock of the system's accuracy and robustness. The assembly and disassembly of the pneumatic cylinder are captured under various scenarios, including correct and incorrect assemblies, missing parts, and misaligned components. These images showcase different assembly stages, individual components, partial assemblies, and fully assembled cylinders. Figure 5 displays captured images showcasing the fully assembled pneumatic cylinder under various lighting conditions and from different viewing angles.  
This comprehensive image collection ensures that the dataset encompasses all potential variations that might occur during pneumatic cylinder assembly. Once collected, the images are categorized based on assembly stage, component type, and assembly correctness. Proper categorization simplifies the annotation process as a crucial step in training the machine learning model. The web-based platform Roboflow [61] was employed for dataset  
Figure 4: Prototype setup.  
Figure 5: Captured images of the fully assembled pneumatic cylinder (complete).  
management and image annotation due to its streamlined interface, comprehensive toolset, and efficient handling of large datasets.  
The dataset comprises over 2290 annotated images, categorized into the following six distinct classes: cylinder, end caps, piston, tie rods, nuts, and complete assemblies. The pneumatic cylinder prototype dataset was carefully selected, covering various instances essential for training a robust quality control system. It is structured to facilitate not only the classification of various components but also the precise detection and location of these elements in assembly scenarios.  
Notably, these instances stand out for their balanced distribution across the defined classes, ensuring that the training process is not biased towards any specific category. Furthermore, their normalized sizes contribute to consistency and computational efficiency during the learning phase.  
Figure 6 illustrates the proof of correlation between different instances or features within the dataset through the correlogram.  
A correlation analysis, represented in Figure 6, visually demonstrates the relationships between different instances or features within the dataset. This correlogram serves as a valuable tool for understanding possible redundancies or distinguishing features, guiding subsequent stages of model development and refinement.  
It is crucial to note that in the YOLO algorithm, the presence of correlated features among the chosen elements, known as anchor boxes, can be advantageous. Anchor boxes in YOLO play an essential role in detecting objects across different scales and aspect ratios. These predefined bounding box shapes aid the algorithm during the object detection process, contributing to the algorithm's accuracy and effectiveness.  
The dataset employed in this study is characterized by its extensive size, diverse representation of assembly conditions, the inclusion of realistic environmental settings, consideration of lighting conditions and viewing angles, and meticulous normalization. These characteristics collectively contribute to a dataset that not only reflects the complexities of pneumatic cylinder assembly but also provides a solid and versatile foundation for training a machine-learning model tailored to the challenges of industrial quality control.  
Figure 6: Correlogram of the different instances in the dataset.