# Object Detection Based on Multiple Information Fusion Net
## 4 Experimental Results  
### Experimental Settings  
Databases and evaluation metrics: Our model was evaluated on two databases: PASCAL VOC [41] and MS COCO [42]. PASCAL VOC is a widely used image database for object detection and classification. In our work, VOC 2007 and VOC 2012 subsets were utilized. VOC 2007 data set contained 9963 annotated images and 24,640 annotated objects, which were composed of three parts: train, validation (val), and test sets. VOC 2012 is an updated version of VOC 2007 data set, which included 11,530 pictures with 20 categories including people, animals (such as cats, dogs and birds), vehicles (such as cars, ships and planes), and furniture (such as chairs, tables, and sofas). Some examples of PASCAL VOC database can be seen in Figure 5. MS COCO database is built by Microsoft, which contained 328,000 images with 2000 object labels. Compared with PASCAL VOC, MS COCO includes natural images and common object images in daily life with more complex background, larger number of targets and smaller size. Thus, the object detection task on MS COCO database was more difficult and challenging. The sample images in MS COCO are shown in Figure 6. In this study, we followed Ref. [35] to adopt average precision (AP) and mean of average precision (mAP) as our evaluation metrics to compare the performances of different approaches. AP, which derived from precision and recall, is one of the popular metrics to measure the accuracy of object detectors. It computes the average precision value for recall value over 0 to 1. The precision and the recall rates are defined as follows:  
\[precision\ =\ \frac{TP}{TP+FP}, \tag{12}\]  
\[recall\ =\ \frac{TP}{TP+FN}, \tag{13}\]  
where \(TP\) represents the number of true positive examples, \(FP\) represents the number of fault positive examples, and \(FN\) represents the number of fault negative examples. mAP is the average of AP on all categories.  
Implementation details: In our experiments, the proposed model was implemented based on Faster R-CNN [7], an open-source framework for object detection built on Tensorflow [56] platform. The VGG-16 [57] and Resnet-101 [19] pre-trained on ImageNet [58] were adopted as backbone networks in our model to extract image feature. When adding the newly fully connected and convolutional layers, they were randomly initialized with a zero-mean Gaussian distribution with standard deviations of 0.01 and 0.001. The message passing module contained two parallel GRU units with shared parameters. All the parameters of the GRU units were initialized based on SIN [35]. Non-maximum suppression (NMS) with intersection over union (IOU) were used for duplicate removal in all experiments.  
Stochastic gradient descent (SGD) was applied to fine tune our network. Each SGD mini batch was composed of 256 randomly sampled object proposals from two randomly chosen images. In each mini-batch, 25% of the RoIs were selected as foreground from object proposals, which had IOU overlapped with a ground-truth bounding box of at least 0.5. We sampled the remaining RoIs from object proposals which had a maximum IOU with ground truth in the interval [0:1; 0:5]. We trained  
Figure 5: Some sample images in PASCAL VOC database.  
Figure 6: Some sample images in MS COCO database.  
our model on a single NVIDIA GeForce GTX TITAN X GPU with 12 GB memory. The experimental parameters, training and test time of our MIFNet can be seen in Table 1.