# Object Detection Based on Multiple Information Fusion Net
## 4 Experimental Results
### Performance Comparisons
(**b**): with scene-level information. Effectiveness of Relation and Relation Settings: In the second ablation experiment, the validity of relationship information is evaluated. Here, the scene-level information is ignored in our model and we only use a set of Relation GRUs for object detection. Experiments are performed on the PASCAL VOC and MS COCO databases, respectively. From the experimental results shown in Tables 7 and 8, we can see that the performance of our model with only relationship information is still superior to the baseline (Faster R-CNN), especially for highly correlated objects. Taking the detection results in Figure 8 as an example. It is clear that due to the introduction of relation information, the tables and chairs which are strongly correlated with each other can be more accurately detected. This indicates the object relationship is very important for detection. Through the above experiments, we can clearly know that both the scene-level information and object relationship are beneficial for detection, and they are indispensable. Figure 9 shows the detection results of some algorithms and our model. It can be seen that our model performs better when detecting small and highly correlated objects (such as driver, table, chair) due to the message \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}^{\text{50}}\)** & **AP\({}^{\text{70}}\)** & **AP\({}^{\text{5}}\)** & **AP\({}^{\text{M}}\)** & **AP\({}^{\text{L}}\)** & **AR\({}^{\text{1}}\)** & **AR\({}^{\text{10}}\)** & **AR\({}^{\text{100}}\)** & **AR\({}^{\text{5}}\)** & **AR\({}^{\text{M}}\)** & **AR\({}^{\text{L}}\)** \\ \hline Baseline & 21.1 & 40.9 & 19.9 & 6.7 & 22.5 & 32.3 & 21.5 & 30.4 & 30.8 & 9.9 & 33.4 & 49.4 \\ scene & **23.5** & **46.0** & **22.1** & **8.2** & **28.1** & **37.5** & **22.7** & **32.5** & **33.1** & **11.8** & **38.0** & **52.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation study on MS COCO 2014 minival. All methods are trained on MS COCO train set. Baseline: Faster R-CNN trained. Scene: only using scene information. The bold characters represent the best result for each column. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}^{\text{50}}\)** & **AP\({}^{\text{70}}\)** & **AP\({}^{\text{5}}\)** & **AP\({}^{\text{M}}\)** & **AP\({}^{\text{L}}\)** & **AR\({}^{\text{1}}\)** & **AR\({}^{\text{10}}\)** & **AR\({}^{\text{100}}\)** & **passing between scene-level information and object relationship. At the same time, for some objects with strong correlation with the scene, the detection result is also well (such as the boats in the sea scene and aeroplanes in the sky scene). Figure 9: Cont. In order to test whether the number of relation modules will influence the detection result of our MIFNet, we also conduct an experiment to compare the performances of the proposed model with different numbers of relation modules. As shown in Table 9, we find that with the increase of the number of relation modules, the detection accuracy of our model will gradually decrease. This may because too many relation modules will make the network over-associate two objects. For example, once an object appears near the table, it will be detected as a chair regardless its feature. Therefore, we choose one module in the experiment. Effectiveness of GRU Settings and the inputs of GRU: In our network, multiple parallel GRU units are used to fuse the information of scene-level context and object relationship. In order to study the effectiveness of different GRU settings, several experiments are conducted. Firstly, we build the message passing module with different numbers (1 to 3) of GRU units and test their performances. From the experimental results in Table 10, it can be found that when the number of stacked GRU units increases from 1 to 2, the mAP decreases. In addition, when the number of stacked GRU units increases from 2 to 3, no significant performance change can be observed.