# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 5 Conclusions  
In conclusion, screw detection plays a vital role in automating the disassembly process, as screws are one of the most commonly used fasteners. Overall, neural networks present an attractive method for fastener detection due to how quickly they can process an image compared to traditional computer vision methods and the availability of tools used to train them. However, it is important to acknowledge that screw detection remains a significant challenge due to the diverse array of configurations in which screws can be employed.  
The objective of this study was to determine the optimal operational parameters for a robotic system when acquiring and analyzing images to detect cross-recessed screws on laptops using a YOLOv5 neural network. It was shown that various factors impact the performance of a robotic system when it comes to screw detection with a neural network. During the data analysis phase, two critical factors emerged as significant: the velocity at which the robot moved and the use of external lighting. Specifically, the best results were achieved when employing a Cartesian robot moving at velocities of either 10 or 15 mm/s and using external lighting for laptops with dark colors and no additional lighting for laptops with lighter colors. When it comes to analyzing images with a neural network, the size of the neural network architecture is important. Notably, a larger neural network, YOLOv5-XL, outperformed a smaller one and resulted in higher detection accuracy. The characteristics of individual screws also impact the system's ability to detect them accurately. The system performed best with screws located on the laptop's surface, without a taper, and positioned away from the laptop's edges. The results also show that the highest detection level occurred with either a large color difference or no color difference between the screws and the laptop case. However, the most critical factor influencing accurate screw detection was found to be the depth at which the screw was recessed into the laptop. A factor that did not have a notable effect on the screw detection accuracy was the number of passes the robot made over the laptop.  
In future research, the focus will be on enhancing the accuracy and reliability of screw detection by implementing measures to reduce false detections. This includes developing advanced filtering techniques to distinguish true detections from false positives, ultimately increasing confidence in the accuracy of detected screws. Furthermore, an exploration of methods to improve the detection of screws recessed into deeper holes, potentially by experimenting with brighter lighting conditions beneath the camera or exploring alternative sensor technologies, such as depth sensors, that can provide better visibility in such scenarios, will be pursued. Additionally, efforts will be made to assess the processing time and hardware demands, particularly in the context of large-scale e-waste applications. These efforts will aim to advance the capabilities of the robotic system and make it more robust in detecting screws in a wider range of challenging conditions.  
Conceptualization, N.M.D. and M.K.J.; methodology, N.M.D. and M.K.J.; software, N.M.D.; validation, N.M.D., M.K.J. and A.D.J.; formal analysis, M.K.J. and A.D.J.; investigation, N.M.D., M.K.J. and A.D.J.; resources, M.K.J.; data curation, A.D.J.; writing--original draft preparation, N.M.D., M.K.J. and A.D.J.; writing--review and editing, N.M.D., M.K.J. and A.D.J.; visualization, N.M.D. and A.D.J.; supervision, N.M.D. and M.K.J.; project administration, M.K.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.