## 5 Experiments and Results
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Detention Method**} & \multicolumn{3}{c}{**AP (\%)**} & \multirow{2}{*}{**TPAP (\%)**} & \multirow{2}{*}{**Test Time (s)**} \\ \cline{2-2} \cline{6-6}  & **Car** & & & & **Person** \\ \hline Faster R-CNN (vgg16) & 74.05 & 62.02 & 43.98 & 60.02 & 0.068 \\ Faster R-CNN(Res101) & 77.80 & 65.73 & 46.06 & 63.20 & 0.157 \\ HyperNet & 78.10 & 72.42 & 47.61 & 66.04 & 0.113 \\ CMS-R-CNN [32] & 77.39 & 60.59 & 42.50 & 60.16 & 0.085 \\ R-FCN & 76.80 & 63.62 & 43.87 & 61.43 & **0.065** \\ Light head R-FCN [30] & 77.77 & 64.49 & 48.78 & 63.68 & 0.119 \\ Our method & **78.96** & **74.12** & **52.49** & **68.52** & 0.108 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of different frames on the FILR dataset. Bold numbers indicate the highest indicator values of all frames. IOU threshold is 0.3. In these experiments, the default method of data augmentation was flipping. The purpose of Ours2 and Ours3 was to verify the alleviation of non-uniform class distribution by instance level data augmentation (ILDA). From the results, we can see that our method improved the detection of a minor category (bicycle) by 3.11%, and also improved the detection of cars by 0.14%, but the detection of people showed a slight degradation by 0.16%. DR selected out 2000 proposals using NMS. Ours3 directly selected positive and negative examples of 1:3 to form 256 mini-batches for training. However, there were many false positive and false negative examples among them. Therefore, training should pay more attention to them. Using loss value as a criterion to measure batch difficulty, we chose 256 batches with the highest loss value for training. The results show that, compared with Ours3, Ours4 had a low-level improvement of 0.45%. In order to further verify the impact of each factor in our frame, we conducted three experiments from Ours5 to Ours7. The results showed that all parts of our frame are indispensable. Figure 5 shows the experimental results of Ours3 to Ours7 compared with Ours4. From Figure 5a, we can see that, without abundant and diverse bicycle data, the frame is not robust to bicycle detection. From Figure 5b, without multi-scale pooling module, the network is not able to adequately capture multi-scale representations, and the detection results are poor in complex environments. From Figure 5c, without OHEM to strengthen the training, the detection will be disturbed in complex environments and noise will be easily misidentified as an object. From Figure 5d, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Detection Method** & **DR** & **MSP** & **ILDA** & **OHEM** & \multicolumn{3}{c}{**AP (\%)**} & \multirow{2}{*}{**mAP (\%)**} \\ \cline{3-3} \cline{5-10}  & & x & x & x & & **Car** & **Person** & **Bicycle** \\ \hline baseline & x & x & x & x & 78.59 & 67.29 & 52.73 & 66.20 \\ Ours1 & \(\surd\) & x & x & x & 78.66 & 71.71 & 52.40 & 67.59 \\ Ours2 & \(\surd\) & \(\surd\) & x & x & 78.96 & 74.12 & 52.49 & 68.52 \\ Ours3 & \(\surd\) & \(\surd\) & \(\surd\) & x & 79.10 & 73.96 & **55.60** & 69.55 \\
**Ours4** & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **79.58** & **74.95** & 55.48 & **70.00** \\ Ours5 & \(\surd\) & x & \(\surd\) & \(\surd\) & 78.87 & 72.20 & 55.49 & 68.85 \\ Ours6 & \(\surd\) & \(\surd\) & x & \(\surd\) & 79.29 & 74.72 & 53.68 & 69.23 \\ Ours7 & x & \(\surd\) & \(\surd\) & \(\surd\) & 79.46 & 73.46 & 52.02 & 68.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of comparative experiments of the different parts in our frame. Bold numbers indicate the highest indicator values of all frames. IOU threshold is 0.3. Figure 5: Comparative results (**a**) between Ours6 and Ours4, (**b**) between Ours5 and Ours4, (**c**) between Ours3 and Ours4, (**d**) between Ours7 and Ours4. Yellow lines indicate correctly-detected objects, red lines indicate misjudged objects, and black lines indicate incorrectly-detected objects.