# Single-Shot Object Detection with Split and Combine Blocks
## 4 Experiments
### Visual Analysis  
In order to further explore the facilitating role of our SC block in feature fusion, we compare the heat maps of multiple feature maps at different depth. Figure 8 demonstrates the qualitative results of feature maps \(\{P_{3},P_{4},P_{5},F_{3},F_{4},F_{5}\}\). Map the values calculated by channel-wise summation to input images to obtain the heat maps of feature maps. The color scheme we adopted is the warm-to-cool color scheme, where the warm colors represent areas of more interest to the network and the cool colors represent areas of less interest to the network. On a whole, the overriding concern of the network is the central area of the object. As shown in the first row of Figure 8, the horse attracts great attention from the feature maps \(P_{5}\) due to the large size. On the contrary, feature maps \(P_{4}\) and \(P_{3}\) take no notice of the object and view them as the background. This indicates that the feature information in the corresponding area on the feature map \(P_{4}\) and \(P_{3}\) is filtered out and do not get involved in gradient propagation during training. However, it does not imply that these feature maps take no effect on the prediction of the horse; they facilitate precise localization. After SC-FPN, the network is more attentive to the central area of the horse as shown in \(F_{5}\), which is conducive to improve detection performance. The analogous circumstance can be observed in the heat maps of the second row. The feature maps \(P_{5}\) and \(F_{4}\) are responsible for detecting the two cows due to the different sizes. After feature fusion by SC-FPN, the feature maps \(F_{5}\) and \(F_{4}\) pay more attention to central areas which contain stronger semantic information; on the contrary, feature maps \(F_{3}\) focus on the areas around the object which contain more geometric information.  
More experiments are added to verify that the good performance of SCNet is mainly due to SC modules, rather than these tricks. The same visualization is performed on a control detector which has exactly the same network structure and tricks as SCNet except for the SC block. The control detector adopts residual block to replace SC block in the backbone, and keeps almost the same number of trainable parameters and convolutional layers as SCNet to achieve a fair comparison. Take the third and sixth rows of Figure 8 as examples. Due to varying sizes, these objects are jointly predicted by three levels of feature maps. The full attention to the center areas reflects the good performance of SCNet, and, after SC-FPN processing, feature maps are more sensitive to critical areas. As for control detector, the heat maps indicate the inferior quality of feature maps, such as the omission of extremely small objects in the upper left corner of the image. After using the SC-FPN, the performance gap becomes more apparent. The SC block leads to a noticeable performance improvement in the box AP from 34.2% to 38.8% on the COCO val split, as shown in Table 3. Some examples of object detection results on the MS COCO test-dev set using an SCNet and control detectors are shown in Figure 9.  
Figure 8: Heat maps of feature maps \(\{P_{3},P_{4},P_{5},F_{3},F_{4},F_{5}\}\), which are extracted from SCNet and a control detector. These detectors complete the network training under the same conditions and tricks to achieve fair comparisons.  
Figure 9: Some detection results of SCNet on the MS COCO test-dev set. The gold boxes indicate the detection results of the control detector.