# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 6 Experiment Results and Discussion
### Experiment Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Mean** & **Gun** & **Knife** & **Wrench** & **Pliers** & **Scissors** \\ \hline CHR & 47.21 & 96.68 & 70.18 & 22.82 & 16.86 & 29.52 \\ GBAD & 47.50 & 90.70 & 77.89 & 20.09 & 27.06 & 21.78 \\ Mask & & & & & \\ R-CNN & 82.24 & 98.02 & 82.91 & 75.98 & 76.80 & 77.48 \\ Proposed & **89.42** & **99.03** & **88.95** & **85.18** & **89.81** & **84.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification mean average precision (%) for SIXray100. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & **Mean** & **Gun** & **Knife** & **Wrench** & **Pliers** & **Scissors** \\ \hline CHR & 79.93 & 99.40 & 87.84 & 66.15 & 85.34 & 60.94 \\ GBAD & 84.75 & 99.72 & 90.06 & 76.80 & 86.02 & 71.13 \\ Mask & 87.74 & 98.76 & 83.32 & 81.44 & 92.06 & 83.10 \\ Proposed & **94.88** & **99.79** & **95.02** & **90.91** & **96.62** & **92.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification mean average precision (%) for SIXray10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & **Mean** & **Gun** & **Knife** & **Wrench** & **Pliers** & **Scissors** \\ \hline CHR & 64.54 & 98.6 & 81.09 & 46.69 & 62.72 & 33.61 \\ GBAD & 66.14 & 95.74 & 85.25 & 54.87 & 53.10 & 41.74 \\ Mask & 86.55 & 98.74 & 77.72 & 78.35 & 89.78 & 77.11 \\ R-CNN & **91.40** & **99.46** & **90.24** & **86.88** & **94.37** & **86.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification mean average precision (%) for SIXray100. the high prediction confidence on true positives and the consistent suppression of false positives caused the significant performance improvement of our proposed approach over the other methods. Figures 9-11 shows the qualitative results of the experiment. Figure 9 shows how our approach was able to accurately segment the detected threat objects even though there being overlap between three objects, i.e., knife, wrench, and gun. However, despite being able to produce segmentations for all the correct detections, our approach is shown in Figure 9 to have had a hard time in accurately delineating overlapping objects when most of them had high-density material properties. Still, for most of the non-overlapping objects, our approach was able to correctly verify the detections. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Approach** & **Gun** & **Knife** & **Wrench** & **Pliers** & **Scissors** \\ \hline \multirow{4}{*}{a} & CHR & 24.0 & 98.30 & 0.06 & 0.00006 & 0.003 \\  & GBAD & 100 & 100 & 0.000007 & 0.000002 & 0.0000002 \\  & Mask & 0 & 97.99 & 0 & 0 & 0 \\  & R-CNN & 0 & 99.98 & 0 & 0 & 0 \\  & Proposed & 0 & 99.98 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{b} & CHR & 99.97 & 0.03 & 0.000004 & 0.02 & 0.00005 \\  & GBAD & 100 & 0.000005 & 0.000006 & 0.000005 & 0.0000008 \\ \cline{1-1}  & Mask & 0 & 0 & 0 & 0 & 93.33 \\ \cline{1-1}  & Proposed & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Multi-label detection confidence (%) for randomly sampled images shown in Figure 8.  
Figure 8: Randomly sampled images from the SIXray100 test set. The images were drawn from the pool of (**a**) positive and (**b**) negative samples. Figure 10 shows the false positive detections that were suppressed by our approach. These were regions in the negative samples that were predicted by Faster R-CNN to have threat objects. Since DeepLabV3+ did not predict any segmentation mask for these regions, they were not included in the final prediction output. We observed an overwhelming number of false positives for the scissors class, which was mostly squashed by the second stage of our approach. It may also be the case that the other approaches also produced too many unsuppressed false positive predictions for the scissors class, causing it to suffer the most degradation compared to other classes. Lastly, Figure 11 shows some of the failure cases of our approach. In these images, Faster R-CNN wrongly predicted suspected regions to which DeepLabV3+ generated segmentation masks.