# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 4 Experimental Results
### Comparison with Other Methods  
Compared to the Mask R-CNN experiment, our experimental results contained 17 pictures with undetectable small targets, while the Mask R-CNN experimental results contained 24 pictures with undetectable small targets. The statistics of the Mask R-CNN detection results are shown in Figure 8. As shown in Figure 10, the comparison shows that in the (0, 0.01) interval, the accuracy of our results was 23%, whereas that of the Mask R-CNN was 10%; further, the accuracy of the results obtained by the Mask Scoring R-CNN was 7%. When the proportion of small targets was in the (0.01, 0.02) interval, the accuracy rate of our experiment was 98.48%, while the accuracy of Mask R-CNN detection results was 95.45% and the correct rate of Mask Scoring R-CNN detection results was 83.33%, and the correct rate in other proportions was 100%. Therefore, the detection results obtained by this method are much higher than those obtained using the traditional small-target detection method.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Parameters** & **Training Time** & **Memory** \\ \hline Baseline & 60.01 M & 1.7 h & 5264 MB \\ Baseline + CBAM & 61.75 M & 1.7 h & 5302 MB \\ Baseline + classifier head & 59.32 M & 1.6 h & 5232 MB \\ Baseline + BiFPN & 96.31 M & 2.1 h & 6203 MB \\ The proposed & 98.32 M & 2.2 h & 6283 MB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval (\%)** & **Targets** & **Correct Number** & **Correct Rate** \\ \hline (0, 0.01) & 100 & 23 & 23\% \\ \hline (0.01, 0.02) & 66 & 65 & 98.48\% \\ \hline (0.02, 0.03) & 71 & 71 & 100\% \\ \hline (0.03, 0.04) & 82 & 82 & 100\% \\ \hline (0.04, 0.05) & 46 & 46 & 100\% \\ \hline (0.05, 0.06) & 28 & 28 & 100\% \\ \hline (0.06, 0.07) & 20 & 20 & 100\% \\ \hline (0.07, 0.08) & 5 & 5 & 100\% \\ \hline (0.08, 0.09) & 7 & 7 & 100\% \\ \hline (0.09, 0.10) & 5 & 5 & 100\% \\ \hline (0.1, -0.25) & 15 & 15 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Target-recognition results of our method.  
Figure 9: Results of different modules.  
_Qualitative comparison_: As shown in Table 7,it can be seen that the four images of the detection results of the Mask R-CNN missed the targets that were detected by the proposed method. From (a)-(d), it is observed that our method detected all the staff on the airport surface. Therefore, the detection effectiveness of our method is better because it accurately and efficiently detected all small targets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Input Image** & **Mask R-CNN** & **Ours** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Target-recognition results of qualitative comparison.  
Figure 10: Results of the quantitative comparison.  
To verify the effectiveness of the proposed method, it was applied to a real airport surface to detect apron personnel. All staff in the figures meet the definition of small targets. As can be seen in Figure 11, all the staff were detected with high accuracy, which proves that the proposed method has a desirable effect on the detection accuracy of small targets on the airport apron. Note that the left side presents the original figure, and the image detected by the proposed method is presented on the right side.