# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 3 Results  
### System Detection Accuracy  
This section presents the findings of the study focused on determining the optimal operating conditions for a robotic system and neural network. To illustrate the results, we present a coordinate map, as shown in Figure 5, which depicts the locations of actual screws on a laptop alongside the locations detected via the neural network, using GATE2 as an example (all laptops and their screw numbers can be found in S1-Coordinate Maps.pdf in the Github repository). In this analysis, a successful detection will typically occur when a blue circle (detected screw) intersects with an orange circle (actual screw), indicating that the neural network correctly identified the screw's location. The output images of the neural net, shown in Figure 6, can be checked to ensure that false detections are not located near a detected screw. Due to the movement of the robotic system between images, the positions of the screws may exhibit slight variations, leading to asymmetrical appearances in the blue circle areas at times. For the GATE2 laptop data, there were a total of 21 screws on the laptop, and in the example trial shown, the neural network successfully identified 18 of them. However, three detections were missing (Screw 1, Screw 8, and Screw 19), and there were two false detections in which features other than screws (in this case specifically, stickers) were mistakenly identified and marked in the graph with isolated blue circles. A further discussion of false detections is provided in Section 3.1, Screw Characteristic Analysis. The information from the coordinate maps could be used to create Table 3, which provides a comprehensive view of true detections, false detections, and the vision accuracy parameter, focusing on the GATE2 laptop and broken down according to trial velocity (tables for all laptops can be found in S2-Vision Accuracy.xlsx in the Github repository). For the GATE2 laptop, the \"Number of True Detections\" column shows the number of correctly identified screws out of a total of 21 on the laptop. Meanwhile, the \"Number of False Detections\" column indicates how many times the system incorrectly identified screws (or clusters of false detections) in non-screw locations. The values in the \"Vision Accuracy Percentage\" column were determined using the formula shown in Equation (3). This formula combines two critical aspects of a successful laptop analysis: achieving a high percentage of true detections and minimizing false detections. The resulting percentage serves as a corrected accuracy measure, accounting for the presence of false detections. It  
Table 4 presents the vision accuracy average for all the laptop models, broken down according to the velocity of the robot. The \"Average of all the Trial Velocities\" column represents the mean vision accuracy across the \"V5\", \"V10\", \"V15\", and \"V20\" columns, and it reveals several key insights. Firstly, it highlights that the YOLOv5-XL model outperformed the YOLOv5-S model. Furthermore, the data suggest that, on average, the results were more favorable when the LED light was employed as opposed to when it was turned off. They also show that the number of passes the robot made over the laptop tended not to cause any change in vision accuracy. When examining columns V5 through V20, a notable trend emerges: medium velocities, specifically velocities of 10 and 15 mm/s, tended to deliver better performance on average within a trial group compared to slower or faster velocities, i.e., velocities of 5 and 20 mm/s. According to these observations, the four best trial groups based on their vision accuracy percentages among various combinations were as follows:  
1. V15_S25_XL_wl--73.4%
2. V10_S15_XL_wl--72.7%
3. V10_S15_XL_nl--69.9%
4. V10_S25_XL_nl--68.8%  
After the trial groups with the highest detection rates were identified, the next step involved examining the disparities in detection rates among different laptops.