# Single-Shot Object Detection with Split and Combine Blocks
## 3 Method
### Backbone and FPN  
A similar structure to other methods, our detector is mainly composed of a backbone, an FPN, and a head [33]. They are applied separately to gather information from the input image, fuse feature maps at different depths to tackle the challenge of scale variation, and generate dense predictions of coordinates, scales, and categories of bounding boxes. We alternately stack multiple residual blocks and SC blocks to build the backbone network. A network is shown in Figure 3. All of the residual block herein is composed of a \(3\times 3\) convolutional layer, a normalization layer, and a skip connection layer followed by a nonlinear activation layer. The down-sampling operation in our experiments relies on the residual block with stride 2. As a result, five down-sampling is conducted in the backbone. The dimension setting of the feature maps refers to the previous network structure; we only use \(1\times 1\) or \(3\times 3\) filters to process the feature information.  
We exploit the SC block to realize the multi-scale feature fusion, and propose a feature pyramid network SC-FPN; the diagram is illustrated in Figure 4. First, we choose feature maps at level 3 to 5 \(\{P_{3},P_{4},P_{5}\}\) from the backbone to formulate the typical top-down FPN. \(\{C_{3},C_{4},C_{5}\}\) indicate the newly generated feature maps. Then, an additional bottom-up pathway is connected behind the FPN to further boost the flow of information. We use the SC block to remodel and enlarge the PANet [34], as can be seen in Figure 5b. Feature maps \(\{N_{3},N_{4},N_{5}\}\) are obtained. These feature maps  
Figure 2: SC blocks with different configurations. (**a**) SC-(2,1,2); (**b**) SC-(3,2,2,4); (**c**) SC-(4,2,2,4,6); (**d**) SC-(4,2,2,4,8).  
with different spatial resolutions are then fused. A common solution is to adjust the dimension and then merge them together by element-wise sum or concatenation. Recent works point out the defect of integrating different resolution feature maps at the same proportion [35]. These feature maps of different resolutions contribute unequally to the detection of objects of varying sizes. To alleviate this issue, we exploit the SC block to merge multi-scale feature maps. As shown in Figure 5c, the feature maps \(\{N_{3},N_{4},N_{5}\}\) are fed to an SC-fusion block to generate newly feature maps \(F_{5}\). In general, the low-resolution feature maps are more important to detect large objects. Therefore, we assign different levels of attention to these feature maps based on their spatial resolution, \(N_{5}\) occupies first place, \(N_{4}\) comes second and \(N_{3}\) is the last. The SC-fusion block takes the \(N_{5}\) as input, and merges the semantic information of \(N_{4}\) and \(N_{3}\) one by one. Sampling depth reflects the level of attention. We employ max pooling layers and the nearest neighbor method to eliminate dimension conflicts in the feature fusion process. \(\{F_{3},F_{4},F_{5}\}\) denote the feature maps generated by SC-fusion blocks that are intended for final prediction.  
Figure 4: The structure of our detector. It employs an SC block to build a backbone network and SC-FPN. The fused feature maps are then delivered to a sub-network to generate dense predictions of boundary box locations and categories.  
Figure 3: The structure of the SC-59 network which consists of five SC blocks and five residual blocks. We employ residual blocks to complete downsampling operations. The output of the last three SC blocks is extracted to construct a feature pyramid network.