# Object Detection Network Based on Feature Fusion and Attention Mechanism
## 4 Experiments  
We performed a series of comprehensive experiments on the dataset, PASCAL VOC 2007 [36] and 2012. The PASCAL VOC 2007 dataset contains 20 object categories. The pixel size of the image varies, and is usually (horizontal view) 500 \(\times\) 375 or (longitudinal view) 375 \(\times\) 500. The mean average precision was used to measure the performance of the object detection network. All experiments were performed on a ThinkStation P510 PC with Intel(R) Xeon(R) E5-2623 2.6 GHZ CPU and Quadro M5000 GPU with an 8 GB memory. Our experiments were implemented in Python with TensorFlow. In our experiments, the AF R-CNN was trained on a training set and the detection results were obtained on the test set (PASCAL VOC 2007 test set). Training data 07 represents VOC 2007 trainval and 07+12 represents the union set of VOC 2007 trainval and VOC 2012 trainval. AF R-CNN and faster R-CNN use the same pre-trained model, VGG-16, which has 13 convolutional layers and three fully-connected layers. All the networks were trained using stochastic gradient descent (SGD) [37]. The initial learning rate was set to 0.001. As AF R-CNN, we re-scaled the images and resized the short side to 600. The initial image needed to be processed. The size of the image was fixed to 224 \(\times\) 224 by mapping the image. Then, the fixed size image was input into the VGG network for feature extraction. We compared the AF R-CNN and faster R-CNN for object detection on the dataset, PASCAL VOC 2007. Our experiments contained two parts. In the first part, we analyzed the effectiveness of each component in the AF R-CNN, including feature fusion and the soft mask branch in the attention module. After that, we compared our network with state-of-the-art results in the PASCAL VOC 2007 dataset. Table 1 shows the results of the different object detection network. The first and second columns represent the results of the Faster R-CNN on the dataset, PASCAL VOC 2007. The third(fourth) column shows the results of the method, which we only added the attention module (feature fusion) in the Faster R-CNN. Additionally, the last two columns show the results of AF R-CNN, which we proposed in the article that contained both feature fusion and the attention module. To understand the benefit of the attention mechanism, we calculated the mean average precision of the object. The attention module in the AF R-CNN is designed to suppress noise while keeping useful information by applying the dot product between the feature map and soft mask. From Table 1, we can see that the performance of the object detection network with the addition of the attention module was improved. In the experiments, we evaluated the effectiveness of the attention mechanism. The Faster RCNN+attention achieved an mAP (mean average precision) of 70.3%, 0.4 points higher than the Faster R-CNN. To understand the benefit of the attention mechanism, we calculated the mAP (mean average precision) of 20 object categories. The attention mechanism can enhance the feature contrast. The attention-aware features were more effective for significant objects, such as the car, boat, and person. However, for small targets, the detection efficiency was slightly reduced, such as the bottle, bird, and plant. This is because the attention mechanism was more effective for significant objects. However, for less significant targets, such as smaller targets and shallower targets, the attention mechanism did not achieve the desired results. The mAP of the detection network Faster R-CNN+fusion was 75.0%, which was 5.1% higher than the Faster R-CNN on the dataset, PASCAL VOC 2007. As we have shown above, this is because the fusion features were more accurate than the deep convolution features. The results benefitted from more informative features. The reasonable resolution of features made for better object detection, especially when the object size was small. Our detection network outperformed the Faster R-CNN when the object size was small.