# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 4 Discussion  
To achieve accurate automatic screw detection in a robotic system with a neural network, various factors come into play. One crucial consideration involves the velocity at which the robot collects data. If the robot moves too quickly, it can lead to blurred features that are hard to discern. Conversely, if the machine moves too slowly, a very large number of images are generated, and there is an increased risk of false detections from one of the categories referred to in Table 6. The paper identifies an optimal medium velocity range of 10 to 15 mm/s, which yielded the best results. Another important factor pertains to the collection of data and the use of external lighting to illuminate the area, particularly when working with laptops. Interestingly, the effectiveness of this parameter is laptop-dependent. In most cases, employing additional light enhances vision accuracy. However, on select laptops, it can result in more false detections, thus diminishing accuracy. Notably, laptops with lighter-colored cases tend to suffer from reduced accuracy when external lighting is used, whereas laptops with darker cases benefit from the added illumination. Lastly, the number of passes the robot makes over the laptop does not impact the detection accuracy of the system. Table 8 presents the results of this study's screw detection compared to other works. Direct comparison is challenging due to the different accuracy metrics used in these papers. For instance, [PERSON] and [PERSON] (2018), [PERSON] et al. (2018), and [PERSON] et al. (2019) reported accuracy as the ratio of correctly detected screws to the total screws, without penalizing false detections. If false detections were accounted for, their reported accuracies would be lower. [PERSON] et al. (2019) employed an accuracy metric that included false detections similar to the one used in this paper, but false detections were added to the total number of screws. Our method goes further by penalizing false detections more severely, subtracting them from the count of true detections across all trials. When using the accuracy metric from (2018), our reported percentages would remain the same or increase. Figure 12: Venn diagram showing combinations of screw characteristics and their corresponding screw detection rate. A—surface screw, B—no taper, C—interior screws, and D—no laptop or screw color difference. A critical parameter tested in the neural network is the network size, which played a crucial role in vision accuracy. Larger networks generally led to better accuracy; however, they also came with the trade-off of increased computational demands and longer processing times. The accuracy of neural networks in detecting screws is significantly influenced by various screw characteristics, such as color and taper. In summary, the system showed a higher accuracy in identifying screws that fall into certain categories. These include screws on the laptop's surface, screws without a taper, interior screws, and screws and laptop cases with a significant color difference or no color difference at all. Upon closer examination of the data, screws with a lower detection rate tended to have an average depth of approximately 0.351\" (about 8.9 mm), whereas screws with a perfect detection rate had an average depth of approximately 0.105\" (about 2.7 mm). When examining screw locations, it becomes evident that exterior screws posed a more significant challenge for the neural network's detection capabilities. They accounted for 60.9% of the screws in the poor detection category while comprising only 31.7% of the screws in the perfect detection category. In contrast, corner screws showed relatively consistent detection rates between the two categories, 17.4% and 17.1%, respectively, suggesting that their location did not significantly impact detection outcomes. Interior screws, on the other hand, exhibited a noteworthy difference in detection rates.