## 5 Experiments and Results
### Comparative Experiment  
In this section, in order to verify that our network is competitive with other mainstream Faster R-CNN based frames, we strictly controlled the experiment conditions. The network structures of all the experiments listed in Table 1 were the same as those designed in original papers. For convenience, all experiments adopted an end-to-end training strategy. In order to prove the superiority of our network structure, we did not use Online Hard Example Mining (OHEM) to pick out 256 proposals generated by a double-layer RPN pyramid. In all the experiments shown in Table 1, we directly selected 256 proposals as a mini-batch for training, where the ratio of positive and negative samples  
Figure 4: Examples of two ratios. CP: common part, CB: current box, AB: another box. was 1:3. Relevant training strategies were the same as those stated in Section 5.1. We used the original FILR dataset to train all frames in the experiment, using flipping as a data augmentation strategy. Compared with Faster R-CNN (vgg16), using a deeper residual network as a backbone can extract more abstract features of the object, resulting in a performance improvement of 3.18%. However, since only the last layer of backbone is used as the input of RPN, the detection performance was not improved much. At the same time, the computational complexity has increased dramatically. Hypernet fuses the output of the first, third, and fifth layers of backbone to get the fused feature map for proposal generation. Additionally, it reduces the channels of the fused feature map in order to avoid an increase of computation. These improvements have positive effects, but the computational cost is still expensive. The purpose of performing a CMS-R-CNN experiment is to verify the role of context in infrared images. We set the area of context to 1.3 times that of the proposal. However, the experimental results show that the effect of context information on detection is very slight, which also verifies that the background information in infrared image is monotonous. Meanwhile, the scale parameter is also difficult to be set up. Both Light head R-FCN and R-FCN use a PSalign pooling layer to extract the local features of objects. The former uses a full connection layer to locate and recognize objects, while the latter uses a voting mechanism. The speed of R-FCN is noteworthy, however, the experimental results show that local features alone are still inadequate for multi-scale object detection. A double-layer RPN pyramid module, PSalign pooling layer, and inception4 module were used in our method. Relevant experiments show that the performance of detecting cars, persons and bicycles has been improved remarkably, while the detection efficiency was maintained at a middle level. In order to verify the usefulness of each network structure improvement and data augmentation, we have carefully designed the following experiments. It is noteworthy that the baseline of the following experiments is Faster R-CNN with VGG16 as backbone and pooling as ROI align. Obviously, using ROI align pooling, we can get more precise locations of proposals. Compared to the original Faster R-CNN with ROI pooling, mean average precision (mAP) was increased by 6.18%. We listed experiment results in Table 2.  
Ours1 was set to verify the improvement of double-layer RPN pyramid (DR). Ours1 uses two RPNs to generate different scale proposals on coarse and fine feature maps in DR. Using NMS to filter redundancy, DR can provide more precise proposals. The results show that DR gives Ours1 a 1.39% improvement over baseline. In order to effectively capture multi-scale features, Ours2 obtains the appearance of corresponding objects on different levels (conv5, conv4). Combining inception4 and PSalign pooling layer, the network can explore more potentials of the multi-scale pooling (MSP) module. The results show that MSP leads to a 0.93% improvement over Ours1.