# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 3 Results
### Screw Characteristic Analysis
This table classifies screws based on specific attributes and then calculates the percentages within each attribute category. To illustrate, consider the \"Deep Screw\" row and the column labeled \"Poor Detection Rate Sample %\". This calculates the percentage of screws classified as deep screws that also had a detection rate of 50% or less. This percentage is determined by dividing the number of screws meeting these criteria by the total count of screws with a detection rate under 50% (23 screws). For comparison, the \"Surface Screw\" row contained no screws classified as both surface screws and screws having a poor detection rate, resulting in a sample percentage of 0%. Consequently, the Deep Screw sample percentage is 100%. The \"Perfect Detection Rate Sample %\" column calculates the percentage of deep screws that achieved a 100% detection rate by dividing the number of screws meeting these criteria by the total count of screws with a 100% detection rate (41 screws). The \"All Screws Sample %\" column provides the overall percentage of deep screws in relation to the entire set of screws (115). Notably, this table ensures that the sample percentages within different categories (e.g., Deep Screw or Surface Screw, Has a Taper or No Taper, etc. ) sum up to 100%, independently of the other categories. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Total Detections** & **Percent Detection Rate** \\ \hline Grate & 42 & 14.44 \\ Protruding shape & 54 & 18.56 \\ Reflective textures & 5 & 1.72 \\ Sticker & 120 & 41.24 \\ Deep hole & 14 & 4.81 \\ Surface shapes or marks & 56 & 19.24 \\ \hline
**Total False Detections** & 291 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: False detections broken down by different categories. Figure 11: Different examples of categories the system found. Note that these images were cropped for visibility. (**a**) grates; (**b**) protruding shape; (**c**) reflective surface; (**d**) sticker; (**e**) deep hole; and (**f**) surface shapes or marks. Finally, the \"All Screws Detection Percentage\" column presents the average detection percentage for screws of a particular category. The system was, on average, able to find 68.8% of all the deep screws and 94.1% of all the surface screws. One should note the reported percentage is not a vision-accuracy percentage because only correct detections were analyzed. This reported percentage represents the average percentage at which a particular screw was discovered across all the trials when the specified condition was satisfied. For example, the 94.1% value for surface screws came from taking the average of the \"Total %\" column in Table 5 for any screw classified as a surface screw over all the laptop models. This column shows how several characteristics significantly influence the ease of detecting screws. First, screws placed on the surface of an object were found to be more easily detectable than those buried deep within the material. Additionally, screws with a tapered shape proved to be more challenging to detect compared to their non-tapered counterparts. The location of the screws also played a role, with interior screws exhibiting a higher detection rate compared to screws situated at corners or on the exterior of objects. Interestingly, the color differentiation on the screws also impacts detection, as screws with a large color difference or no color difference at all were easier to spot than those with a medium color difference. To better understand and view these screw characteristic sample percentages and breakdowns, the four screw characteristics were translated into binary data. All possible combinations were analyzed to view the effect that the four screw characteristics had on the screw detection within the data set. Characteristics associated with a high detection rates (surface screw, no taper, internal screws, and no color difference) were labeled as \"1\", and characteristics with a negative association were labeled as \"0\". In this analysis, corner screws were grouped with exterior screws.