# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 4 Experimental Results
### Ablation Experiments  
According to the pixel size of all targets in the dataset, in which the maximum pixel was 45 \(\times\) 21 and the maximum proportion of the target size was 0.12%, the number of correctly identified targets in 11 proportional intervals was counted. The correct rate of identification using different methods was compared. The results of the ablation experiment are as follows. Attention module-CBAM: In comparing Tables 1 and 2, it can be seen that the detection results of the attention mechanism are better than the baseline, which were increased by 8% in the range of (0, 0.01) and 6.06% in the range of (0.01, 0.02), and the results of other ranges  
Figure 8: Dataset details. Figure 7: Airport small-target dataset. reach 100%, which shows that the attention mechanism added in this study had a more noticeable effect and is conducive to the detection of small targets. Feature fusion-BiFPN: It can be seen from Table 3 that adding the feature fusion module BiFPN alone, compared with Mask Scoring R-CNN from the experimental results, increased the detection accuracy in the range of (0, 0.01) by 3%, and the detection accuracy in the range of (0.01, 0.02) was increased by 4.55%. Overall, there were seven more small targets detected than in the baseline. Classifier head: From Table 4, it is visible that the detection head added in this study also had noticeable effects. The detection accuracy in the range of (0, 0.01) was improved by 2%, in (0.01, 0.02) by 7.58%, and the total number of detections was increased by nine small targets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval (\%)** & **Targets** & **Correct Number** & **Correct Rate** \\ \hline (0, 0.01) & 100 & 7 & 7\% \\ \hline (0.01, 0.02) & 66 & 55 & 83.33\% \\ \hline (0.02, 0.03) & 71 & 70 & 98.59\% \\ \hline (0.03, 0.04) & 82 & 81 & 98.78\% \\ \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, \(\sim\)0.25) & 15 & 15 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Target-recognition results of Mask Scoring R-CNN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval (\%)** & **Targets** & **Correct Number** & **Correct Rate** \\ \hline (0, 0.01) & 100 & 15 & 15\% \\ \hline (0.01, 0.02) & 66 & 59 & 89.39\% \\ \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, \(\sim\)0.25) & 15 & 15 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Target-recognition results of CBAM. Our method: As shown in Table 5, the proposed method, or adding the above three in specific positions, obtained the best results. The range of (0, 0.01) increased by 16%, the range of (0.01, 0.02) increased by 15.15%, and the overall number of small targets detected increased by 27, which effectively proves that the proposed method better detects small targets. According to the results, the detection method's accuracy was improved due to the use of the Mask Scoring R-CNN. When only the CBAM was included, the detection performance was the most improved (Figure 9), compared with the baseline in the scale range of (0, 0.01). That is, the detection accuracy was improved by 8%. Compared with the baseline, the method proposed in this study was improved 16% and 15.15% in the range of (0, 0.01) and (0.01, 0.02), respectively; all other sections were also improved. As can be seen in Table 6, the model parameters exhibit certain changes while the remaining changes are minor. This is sufficient to prove that the proposed method has a higher detection accuracy for small targets.