## 5 Numerical Results and Discussion
### Discussion and Future Works  
Our proposed method employs the YOLO algorithm to detect objects. The detection trigger is set at the point where a pin is fastened, coinciding with the worker's hand appearing and then disappearing. Additionally, we assessed the performance of our trained model using nine different regression algorithms. We anticipate that by ensuring an equal distribution of labeling and class-specific data, we can develop a more robust model. However, our methodology might be improved by adopting state-of-the-art object-detection/pose-estimation algorithms such as YOLO-NAS [26]. YOLO-NAS is designed to detect small objects, enhance localization accuracy and improve the performance-per-compute ratio for real-time application in edge-device environments. While it can be applied to pose estimation, the focus of our proposed paper is not on estimating the pose of workers, but rather on identifying fastening locations and timings in manual assembly.  
For future work, we aim to extend the capabilities of our system by integrating more advanced versions of YOLO-NAS that are optimized for even lower computational overhead and greater efficiency on edge devices. This would enable us to handle more complex scenes in manual assembly environments with higher accuracy and faster processing times. Additionally, we plan to explore the feasibility of adapting our system for real-time pose estimation of workers to further enhance safety and ergonomics in industrial settings. These improvements will contribute to smarter, more adaptive automation technologies in manufacturing processes.  
Also, in our future research, we will also conduct a comparative analysis of our model against the YOLO-NAS Pose models, which have demonstrated state-of-the-art accuracy and latency on the COCO Val 2017 dataset. Specifically, the nano version of YOLO-NAS Pose, capable of achieving output speeds up to 425 fps on a T4 GPU and its larger counter  
Figure 15: Regression results of the screwing position of the manual assembly process corresponding timing (frame).  
part, which reaches up to 113 fps, will be evaluated as potential competitors. By assessing these models, we aim to benchmark our system's performance and identify areas for enhancement, ensuring that our solution remains competitive in high-speed, high-accuracy applications in industrial environments.  
In our upcoming research efforts, we will incorporate improvements to the loss functions used during the training phase, as inspired by [PERSON]'s advancements. We plan to enhance our model's accuracy for both bounding box detection and pose estimation by adopting a dual metric approach. Alongside the traditional Intersection by Union (IoU) score, we will also incorporate an Object Keypoint Similarity (OKS) score, which assesses the accuracy of predicted key points against actual key points. Furthermore, we will explore the implementation of the OKS forward regression method, which has been shown to outperform the conventional L1 and L2 loss methods in similar applications. This advancement will potentially lead to more precise and reliable model predictions in real-world scenarios.