# Object Detection Based on Multiple Information Fusion Net
## 4 Experimental Results
### Performance Comparisons  
PASCAL VOC Database: The performance of our MIFNet was compared with some classical and state-of-the-art object detection methods, including Fast R-CNN [16], Faster R-CNN [7], SIN [35], ION [32], CPF [40], and so on. The experimental results on data set VOC 2007 test and VOC 2012 test are shown in Tables 2 and 3 respectively. All the experimental results of comparison approaches are quoted from their corresponding literatures. From these tables, the following points can be observed. Firstly, Fast R-CNN and Faster R-CNN are classical two-stage approaches, while SSD is a classical one-stage approach. Since the relationship between objects and context information were neglected in them, their performances were inferior to other approaches. Secondly, by utilizing the spatial recurrent neural networks and semantic segmentation, ION and CPF took global contextual information into account. Therefore, they outperformed the classical approaches such as Fast R-CNN, Faster R-CNN, and SSD. Thirdly, SIN considered both the scene context information and object relationships. However, the relationship in SIN was established only by geometric structure of the objects, which neglected the objects' appearance information. As a result, its performance was still worse than the proposed MIFNet. At last, the proposed approach leveraged the attention mechanism to adaptively establish the relationship between objects, which considered both geometric and appearance information. Besides, the scene-level information was also introduced into the model. Thus, our MIFNet greatly improved the detection accuracy of some small and highly correlated objects (such as chair, boat, plant, tv) and achieved the best performance on PASCAL VOC database. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Train** & **Test** & **Backbone** & **Iterations** & **Learning** & **Run Time** & **Test Time** \\
**Database** & **Database** & **Backbone** & **Setting** & **Rate** & **(Sec/Img)** & **(Sec/Img)** \\ \hline VOC 2007 & & \multirow{4}{*}{VOC 2007} & first 80k & \multirow{4}{*}{0.0005} & \multirow{4}{*}{0.35} & \multirow{4}{*}{0.17} \\ trainval + VOC 2007 & & & iterations & & & \\ \cline{4-5} \cline{5-6}  
MS COCO database: In order to further verify the effectiveness of our proposed method, MS COCO database was utilized. The object detection results of different methods on this database are tabulated in Table 4. In this table, AP was averaged precision across all object categories and multiple intersection over union (IOU) values from 0.5 to 0.95, AP\({}^{50}\) denotes the mAP at IOU = 0.50, AP\({}^{70}\) denotes the mAP at IOU = 0.70, average recall (AR) represents the average recall rate averaged over all categories and IOU thresholds. AR\({}^{1}\), AR\({}^{10}\) and AR\({}^{100}\) denote the maximum recall rate of the fixed number (1, 10, 100) of objects detected in each image, AR\({}^{\rm S}\), AR\({}^{\rm M}\) and AR\({}^{\rm L}\) represent the recall rate of small (area smaller than 32\({}^{2}\)), medium (area between 32\({}^{2}\) and 96\({}^{2}\)) and large (area bigger than 96\({}^{2}\)) objects respectively. From Table 4, we can get the following observations. Firstly, since ION takes global context information into consideration, its performance is better than the classical approaches such as Fast R-CNN, Faster R-CNN and YOLOv2. Secondly, SIN outperforms ION, which indicates the object relationship is important for object detection. Finally, the proposed MIFNet performs best on MS COCO database because it establishes the relationship between objects by both geometric and appearance information adaptively and takes the scene-level information into account. In summary, these observations are consistent with the experimental results of PASCAL VOC database.