# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 2 Object Detection
### Evolutionary Trends  
The evolution of object detection algorithms has been a dynamic interplay between the two-stage and one-stage approaches, each catering to specific requirements and challenges. While two-stage algorithms excel in accuracy but often sacrifice speed, one-stage algorithms offer real-time capabilities at the expense of accuracy, especially for small objects and complex scenes [43; 44]. Object detection, a fundamental task in computer vision, plays a crucial role in various applications, including autonomous driving, video surveillance, and medical imaging. Traditional object detectors, such as R-CNN, Faster R-CNN, and YOLO, have revolutionized image understanding by enabling the accurate detection of objects with upright orientations. However, these methods encounter significant challenges in handling objects with arbitrary rotations, which are prevalent in real-world scenarios. To address this challenge, researchers have delved into the domain of rotated object detection, exploring novel techniques to effectively identify and localize rotated objects. A significant step forward is the introduction of adaptive rotated convolution (ARC) [45]. ARC addresses the limitations of standard convolutions, which treat all input pixels equally, regardless of their orientation. ARC kernels dynamically rotate to match the orientations of objects, enabling the network to extract more informative features. This adaptive rotation mechanism significantly enhances the accuracy of rotated object detection, surpassing the performance of traditional methods. To handle the increased complexity of rotated objects efficiently, [PERSON] et al. [46] proposed an adaptive object detection system based on early-exit neural networks. This system employs a multi-scale feature extraction backbone and early-exit branches to progressively refine object detection results. Early-exit branches discard redundant features, reducing computational costs while maintaining detection accuracy. This adaptive architecture demonstrates a remarkable performance when detecting rotated objects compared to traditional single-scale detectors. [PERSON] et al. introduced Rank-DETR [47], a method that synergistically combines object detection and instance segmentation techniques to achieve high-quality object detection results. Rank-DETR utilizes a dual-stage architecture as follows: first, it identifies object proposals and then refines their locations and identifies their boundaries. This iterative refinement process ensures precise object localization and segmentation. Additionally, Rank-DETR employs a novel ranking loss function that prioritizes accurate object detection, further boosting performance. These studies highlight the transformative impact of rotated object detection research. ARC, the adaptive convolution operation, addresses the challenge of arbitrary object orientations by dynamically rotating convolution kernels, leading to substantial improvements in rotated object detection accuracy. The adaptive object detection system based on early-exit neural networks demonstrates the efficient handling of rotated objects by refining detections at multiple scales and reducing computational resources without compromising accuracy. Rank-DETR, with its dual-stage architecture and ranking loss function, achieves high-quality object detection by prioritizing precise localization and segmentation. These advancements in rotated object detection open new avenues for the development of more robust and versatile object detection systems that are capable of effectively handling real-world scenarios with a wide range of object orientations. Additional references [48] propose a novel feature extraction module incorporating rotation information, improving feature representation, and enhancing rotated object detection performance. Reference [49] introduces a rotation-aware feature extraction mechanism that utilizes attention modules to focus on salient features, leading to more accurate rotated object detection.