# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 3 Results
### Screw Characteristic Analysis  
This section displays the results of the analysis performed on the screw characteristics of all 115 screws found on the laptops. Here, the dataset was divided into 16 trials through  
Figure 8: Vision accuracy percentage for four different trials broken down by laptop. Figure 7: Vision accuracy percentage results across all trials broken down by laptop. which the results and statistics of individual screws could be tallied. The trials included varying LED lighting conditions (on and off), as well as different velocities (10 and 15 mm/s) and passes (15 and 25), and employing different sizes of the YOLOv5 neural networks (S and XL). For each laptop and in each of these trial runs, data were collected for both true detections and false detections, with location and classification information recorded for the false detections. These detection results were then aggregated to calculate the overall percentage of detection for each screw, which could range from 0% to 100%. Additionally, all data related to false detections were compiled and subjected to further analysis. The results depicted in Figure 9 show the percentage at which the individual screws were detected over the 16 trials. For six screws, a 0% detection rate was recorded over all the trials, while 23 screws exhibited a detection rate of 50% or less. In contrast, 41 screws demonstrated a perfect detection rate, being identified 100% of the time. In terms of terminology, screws with a detection rate of 50% or lower were considered to have a \"poor detection rate\", whereas screws consistently identified with a 100% rate were categorized as having a \"perfect detection rate\". Data collection for each of the seven laptops involved recording information for every screw across all trials, and these data were organized into a table format. For illustration, the table for GATE2 is presented in Table 5. The rows are organized by screw number, which corresponds to the screw numbers on the coordinate plot map of the laptop in Figure 5b. Rows F1 through F6 designate specific regions on the laptop where false detections were pinpointed, and they are shown in Figure 10. The regions of false detection for the other laptops can be found in S3-False Detection Regions.pdf in the Github repository. It should be noted that the number of regions found are not fixed; they varied between 6 for GATE2 and 21 for DELL1. Meanwhile, the columns of the chart were organized to represent individual trials, with a value of 0 indicating a missed detection and a value of 1 signifying a correct detection. The column \"Sum\" sums up all of the detections for an individual screw or false detection, and the column \"Total %\" is the sum total divided by the 16 trials. The data for the rest of the laptops can be found in S4-Screw Characteristics.xlsx in the Github repository in their corresponding worksheet. Figure 9: Number of occurrences where an individual screw was detected over all trials grouped according to percentages. Utilizing the tables for all the laptops makes it feasible to categorize the false detections into six distinct groups. These groups include grades, protruding shapes, reflective surfaces, stickers, deep holes, and surface shapes or marks. Visual examples of each of these groups are shown in Figure 11, and the total number of detections and their percentages are tallied in Table 6. These numbers show that, over the 16 trials, there were 291 total false detections. Stickers were the cause of most of the false detections, while reflective textures and deep holes caused the least amount of false detections. Figure 10: Locations of the false detections on the GATE2 laptop labeled F1–F6. Here F1, F2, F5, and F6 are classified as stickers, and F3 and F4 are classified as protruding shapes. \begin{table}
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Table 7 offers a comprehensive breakdown of screw characteristics and their associated sample percentages.