# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 2 Object Detection
### One-Stage Object Detection Algorithms  
While two-stage algorithms achieved impressive accuracy, the computational overhead of region proposals limited their real-time practicality. In response to this challenge, one-stage object detection algorithms emerged, aiming to perform object classification and localization in a single pass. This one-stage approach is exemplified by the YOLO (You Only Look Once) family of algorithms and the SSD (Single Shot MultiBox Detector) algorithm. YOLO, introduced by [PERSON] et al. in 2016 [28], divided the input image into a grid and directly predicted bounding boxes and class probabilities from grid cells. This design significantly reduced the computation time, enabling real-time object detection [29,30]. Although YOLOdemonstrated competitive accuracy, its weakness lay in accurately detecting small objects due to the inherent nature of its grid-based architecture. By contrast, SSD, introduced by [PERSON] et al. in 2016 [31], employed a different strategy. It utilized a set of predefined default bounding boxes with varying aspect ratios and sizes, predicting object classes and offsets for these boxes. This approach allowed SSD to effectively address the challenge of detecting objects at different scales, enhancing its accuracy in small object detection. However, SSD still faces the task of matching the accuracy of two-stage approaches for larger and more complex scenes. The YOLO series, culminating in YOLOv8 in 2023, has shown significant improvements in accuracy and speed, making it an increasingly competitive solution compared to two-stage methods [32; 33; 34]. Figure 1 presents a concise timeline, highlighting the historical dates corresponding to various versions of the YOLO algorithm from its inception to the latest version, YOLOv8. Throughout the historical evolution of the YOLO algorithm, specific versions have stood out, playing pivotal roles in shaping the landscape of object detection. YOLOv1 introduced the concept of one-stage object detection, revolutionizing the field by directly predicting bounding boxes and class probabilities in a single pass. Despite its relative simplicity, YOLOv1 showcased the potential of real-time object detection, setting the stage for further advancements. YOLOv3 marked a substantial leap in accuracy and versatility by introducing a feature pyramid network and multiple detection scales. This version improved detection performance across various object sizes, solidifying YOLO's reputation as a robust object detection algorithm [35]. YOLOv4 represented a significant milestone by substantially improving both accuracy and speed through optimized architecture and advanced techniques. Achieving state-of-the-art performance, YOLOv4's holistic approach to accuracy and speed propelled it to the forefront of object detection algorithms [36; 37]. While YOLOv8 may not be as widely recognized as its predecessors, it has gained prominence due to reported advancements in its accuracy. Positioned as the latest effort to refine the algorithm, YOLOv8 demonstrates a continued commitment to enhancing object detection performance, particularly in scenarios involving small objects and complex scenes [36]. The strategic selection of YOLOv8 for this study is supported by its robust performance and specific advantages that align with the requirements of the proposed quality control application, namely [38; 39; 40; 41; 42]. Advancements in Accuracy: YOLOv8 has demonstrated notable advancements in accuracy compared to its predecessors. The algorithm has been refined to achieve higher precision in object detection. This increased accuracy is particularly crucial in quality control applications, where the identification of components and defects demands a high level of reliability. Figure 1: YOLO timeline. Suitability for Near-Real-Time Processing:  
YOLOv8 strikes a balance between accuracy and speed, making it suitable for near-real-time processing. In industrial quality control scenarios, immediate feedback is essential for timely corrective actions.