# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 5 Results and Discussion
### Multi-Class Comparison Experiments  
The experiments also compare the multi-class detection performance of the base RetinaNet and RetinaNet using HEM. Three general detectors, YOLOv3, YOLOv4, and FRCN-OHEM are added as contrast. For RetinaNet, we use ResNet-50 as the feature extraction network and focal loss as the classification loss function. The evaluation criteria used are mAP and AP. The percentage of hard examples used in the HEM process is 10%, and the number of iterations is 20. The experimental results are shown in Table 4. We can see that the second method achieves better detection performance, and both are better than the results without adding HEM. Obviously, among the basic detectors YOLOv3, YOLOv4, FRCN-OHEM and RetinaNet, RetinaNet has the best mAP. Comparing Experiment \"RetinaNet\" and \"RetinaNet + HEM\({}_{x}\)\", the HEM technique improves the accuracy of the model in detecting the \"CL, CK\" class. However, due to the deletion of some easy samples in the HEM process in Experiment \"RetinaNet + HEM\({}_{E}\)\", the accuracy of the model for \"GA, CP\" decreased. In Experiment \"RetinaNet + HEM\({}_{A}\)\", the detection accuracy of all categories is no lower than that of Experiment \"RetinaNet + HEM\({}_{E}\)\", and the detection accuracy of \"CL, CK, WB\" category is improved. These experiments show that our proposed HEM approach can improve the detection accuracy of hard examples in terahertz images, and the training set construction approach of adding hard examples directly to the training set has better results. To compare the performance of different models more intuitively, we plot the results of Table 4 as a radar map with additional information on the pixel size of the bounding box for each class, as shown in Figure 6. Overall, the detection AP of each model is positively correlated with the pixel bounding box size of the target. The larger the bounding box the higher the detection AP of the model, and vice versa. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Method** & **GA** & **KK** & **SS** & **MD** & **CK** & **WB** & **KC** & **CP** & **CL** & **LW** & **UN** & **mAP** \\ \hline YOLOv3 & 67.15 & 81.69 & 45.56 & 0.0 & 16.39 & 11.29 & 25.00 & 60.74 & 18.57 & **75.43** & **30.35** & 39.29 \\ YOLOv4 & 58.05 & 82.72 & **67.27** & 0.0 & 12.50 & 34.72 & 8.33 & 61.62 & 54.79 & 61.02 & 14.23 & 41.39 \\ FRCN-OHEM & 83.39 & 81.69 & 66.67 & 0.0 & 16.39 & 31.56 & 37.38 & 63.21 & 18.57 & 44.23 & 22.38 & 42.32 \\ RetinaNet & **88.07** & **100.0** & 65.24 & 0.0 & 22.92 & 52.38 & **80.56** & **69.00** & 45.28 & 49.44 & 22.26 & 54.16 \\ RetinaNet+HEM\({}_{E}\) & 87.67 & **100.0** & 65.00 & 0.0 & **25.01** & **53.12** & **80.56** & 68.48 & **47.00** & 49.44 & 22.23 & 54.41 \\ RetinaNet+HEM\({}_{A}\) & **88.07** & **100.0** & 65.24 & 0.0 & **25.01** & **53.12** & **80.56** & **69.00** & **47.00** & 49.44 & 22.86 & **54.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of detection AP for each category in HEM experiments. The results with underline are the worst. and the results with **bold** are the best. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hard Example Ratio (\%)\textbackslash{}Training Loop** & **10** & **20** & **30** \\ \hline
0 & 68.01 & 68.01 & 68.01 \\
5 & 69.55 & 69.57 & 69.49 \\
10 & **69.58** & **69.63** & **69.59** \\
20 & 69.56 & 69.60 & 69.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparative experimental results of HEM with different parameters for single-class detection. Among them, the results with underline are the worst, and the results with **bold** are the best. As shown in Table 4, the detection accuracy of all detectors for \"MD\" is 0. This is because the number of the object in test set is very small, and \"MD\" is similar to \"CK\" in appearance. The difference between the two categories is very small, which not only leads to a detection rate of 0 for \"MD\" but also seriously affects the detection rate of \"CK\".