# Object Detection Based on Multiple Information Fusion Net
## 4 Experimental Results
### Performance Comparisons
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Net** & **mAP** & **Aero** & **Bike** & **Bird** & **Bout** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cow** & **Table** & **Dog** & **Honse** & **Mblike** & **Person** & **PLANT** & **Sheep** & **Sofa** & **Train** & **Tv** \\ \hline Fast R-CNN & V & 70.0 & 77.0 & 78.1 & 69.3 & 59.4 & 38.3 & 81.6 & 78.6 & 86.7 & 42.8 & 78.8 & 68.9 & 84.7 & 82.0 & 76.6 & 69.9 & 31.8 & 70.1 & 74.8 & 80.4 & 70.4 \\ \hline Faster R-CNN & V & 73.2 & 76.5 & 79.0 & 70.9 & 65.5 & 52.1 & 83.1 & 84.7 & 86.4 & 52.0 & 81.9 & 65.7 & 84.8 & 84.6 & 77.5 & 76.7 & 38.8 & 73.6 & 73.9 & 83.0 & 72.6 \\ \hline SSD2014 & V & 75.1 & 79.8 & 79.5 & 74.5 & 63.4 & 51.9 & 84.9 & 85.6 & 87.2 & 56.6 & 80.1 & 70.0 & 85.4 & 84.9 & 80.9 & 78.2  
Effectiveness of scene-level information: For the purpose of verifying the effectiveness of each part in our proposed method, some ablation experiments are carried out. Here, we employ the VGG-16 as the backbone of our MIFNet. In the first experiment, only the scene-level information is considered to update the object feature. As shown in Tables 5 and 6, applying scene-level information achieves a better mAP of 75.8% compared with the baseline (Faster R-CNN without scene-level information and object relationships) on PASCAL VOC 2007. On the bigger database MS COCO, a better mAP of 23.5% can be obtained. We find that introducing the scene-level information can improve the detection performances in certain categories, including bike, bottle, chair, plant, TV, and so on. Especially, the average accuracy of plant has increased by more than 10%. These results are not surprising since these categories are usually highly relevant to the context of scene. From Figure 7, we can clearly see that the detection result in (b) with scene-level information is more accurate than the detection result in (a) without scene-information. This may because the probability of plant appearing increases with the introducing of balcony information. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Net** & **AP** & **AP\({}^{\textbf{S0}}\)** & **AP\({}^{\textbf{P0}}\)** & **AP\({}^{\textbf{S}}\)** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** & **AR\({}^{\textbf{1}}\)** & **AR\({}^{\textbf{10}}\)** & **AR\({}^{\textbf{100}}\)** & **AR\({}^{\textbf{S}}\)** & **AR\({}^{\textbf{M}}\)** & **AR\({}^{\textbf{L}}\)** \\ \hline Fast R-CNN [16] & V & 20.5 & 39.9 & 19.4 & 4.1 & 20.0 & 35.8 & 21.3 & 29.5 & 30.1 & 7.3 & 32.1 & 52.0 \\ Faster R-CNN [7] & V & 21.1 & 40.9 & 19.9 & 6.7 & 22.5 & 32.3 & 21.5 & 30.4 & 30.8 & 9.9 & 33.4 & 49.4 \\ YOLOv2 [27] & V & 21.6 & 44.0 & 19.2 & 5.0 & 22.4 & 35.5 & 20.7 & 31.6 & 33.3 & 9.8 & 36.5 & 54.4 \\ ION [32] & V & 23.0 & 42.0 & 23.0 & 6.0 & 23.8 & 37.3 & 23.0 & 32.4 & 33.0 & 9.7 & 37.0 & 53.5 \\ SIN [35] & V & 23.2 & 44.5 & 22.0 & 7.3 & 24.5 & 36.3 & 22.6 & 31.6 & 32.0 & 10.5 & 34.7 & 51.3 \\ SSD321 [24] & R & 28.0 & 45.4 & 29.3 & 6.2 & 28.3 & 49.3 & 25.9 & 37.8 & 39.9 & 11.5 & 43.3 & 64.9 \\ DSSD321 [24] & R & 28.0 & 46.1 & 29.2 & 7.4 & 28.1 & 47.6 & - & - & - & - & - & - \\ R-FCN [17] & R & 29.9 & 51.9 & - & 10.8 & 32.8 & 45.0 & - & - & - & - & - & - \\ SSD513 [24] & R & 31.2 & 50.4 & 33.3 & 10.2 & 34.5 & **49.8** & 28.3 & 42.1 & 44.4 & 17.6 & **49.2** & **65.8** \\
**MIFNet(ours)** & V & 29.3 & 53.6 & 29.2 & 10.3 & 34.7 & 46.6 & 26.3 & 36.9 & 37.5 & 13.5 & 43.7 & 59.3 \\
**MIFNet(ours)** & R & **32.1** & **55.3** & **33.5** & **15.8** & **36.8** & 47.7 & **28.4** & **42.4** & **43.4** & **24.2** & 49.0 & 61.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection results on MS COCO 2014 minival. Train set: trainval35k: MS COCO train + 35k val. “V” and “R” denote the model uses VGG-16 and Resnet-101 as backbone networks, respectively. The bold characters represent the best result for each column. Figure 7: The detection results with scene-level information or without scene-level information. (**a**): without scene-level information.