## 2 Method
### Multi-Scale Locating Feature Pyramid Network  
In FPN, in order to supplement the low-level features with more high-level semantic information, the high-level features inside the FPN are upsampled and added. Although the top-down feature fusion operation allows the low-level features to obtain rich high-level semantic information, the high-level features output by FPN still have the problem of missing spatial location information. In this paper, we propose a Multi-Scale Locating Feature Pyramid Network (MSL-FPN). SIR is used to supplement the multi-scale spatial location information of the high-level feature maps of FPN, so that the high-level features of FPN can be more detailed and the localization ability of the network is enhanced.  
MSL-FPN uses low-level features of the backbone network to map spatial location information and multi-scale receptive fields into high-level features. Firstly, the backbone network input feature map is subjected to a 7 \(\times\) 7 convolution and 3 \(\times\) 3 Max pooling to obtain C1, which is passed through four convolution layers of the backbone network in turn to obtain the output features C2, C3, C4, C5. Then, the size of each feature channel is uniformly compressed from 256, 512, 1024, 2048 to 256 by a 1 \(\times\) 1 convolution. Finally, the feature map of the first layer is upsampled by bilinear interpolation from top to bottom in turn and added to the feature map of the next layer to obtain P3, P4, P5. The SIR is used to map the high-level features P3, P4, P5 with C1 as the low-level features. The specific operation is to make C1 generate different scale dilated convolution feature maps through spatial information refinement sub-modules SIR-subl, SIR-sublII, and SIR-subIII, respectively. After that, the result is added with P3, P4, P5, respectively, and operated through a 3 \(\times\) 3 convolution, and finally the output F3, F4, F5 of MSL-FPN can be obtained. The specific network structure diagram is shown in Figure 1, MSL-FPN part. The expression of feature hole mapping in MSL-FPN is as follows:  
\[F_{i}=Conv_{3\times 3}(SIR_{6-i}(C_{1})+P_{i_{i}},i\in[3,5] \tag{5}\]  
where \(Conv(\cdot)\) represents the convolution operation with a 3 \(\times\) 3 kernel, \((F_{3},F_{4},F_{5})\) and \((P_{3},P_{4},P_{5})\), respectively, represent the feature graphs in MSL-FPN (F3, F4, F5) and (P3, P4, P5); \(SIR(\cdot)\) denotes the 6-i submodule of SIR; \(C_{1}\) is the first feature map C1 generated by convolutional pooling in the input backbone network shown in Figure 1.  
Figure 2 shows the feature map visualization results of Layer C, Layer P, Layer F, and Layer C in MSL-FPN after the spatial information refinement sub-module. It can be seen from the results that the feature extraction effect of Layer P is significantly enhanced after the feature mapping of Layer C, which verifies that MSL-FPN can extract more key semantic features from feature maps. Figure 3 is the heat map visual comparison of the output features of the original network FPN and MSL-FPN in F3, F4, F5, P6, P7. The color of the heat map varies from blue to red, indicating that the heat value is from low to high. The higher the aggregation degree of the high heat value area in the heat map in the target area, the higher the attention of the network to the target area. It can be clearly seen from Figure 3 that MSL-FPN has a higher degree of high thermal value aggregation in the target area than the original network FPN, and the degree of high thermal value aggregation in the target area increases with the depth of the feature map, indicating that MSL-FPN can extract the spatial location information of low-level features to supplement the high-level features so as to locate the target more accurately.  
Figure 3: Comparison of FPN and MSL-FPN heatmaps.  
Figure 2: The feature maps of MSL-FPN mapping effect.