# An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information
## 4 Our Proposed Intelligent Weighted Object Detector
### Internal Structure of the Model
Reuse feature maps Inspired by GhostNet, which generates redundant feature maps when the feature maps go through multiple convolutional layers, and the fact that generating redundant maps causes the problem of computational redundancy, the idea of feature map reuse is proposed. The feature map reuse equation (5) is as follows. \[x_{out}=cat(x_{in1},x_{in2}).\] (5) where \(x_{in2}\) is the feature map that has been generated in the network and has the same dimensions and size as the input feature map \(x_{in1}\). When the number of channels of the output feature map is twice that of the input feature map, channel concatenation of the input feature map \(x_{in1}\) with \(x_{in2}\) can reduce the convolutional up-dimensioning operation for deeper feature maps with higher dimensionality. Thus, reusing fea  
Figure 4: Internal structure of our proposed intelligent weighted object detector. Figure 5: Local-feature extractor. ture maps can effectively avoid the dimensionality increase operation using \(1\times 1\) convolution and reduce a large number of convolution parameters. ```
0: Feature map \(x\epsilon R^{batch\times H\times W\times C}\)
0: Global semantic information-rich feature maps \(x\epsilon R^{batch\times H\times W\times C}\)
1: According to \(\theta(x_{i})=W_{\theta}x_{i}\), \(\phi(x_{j})=W_{\phi}x_{j}\), use the embedding weights \(W_{\theta}\) and \(W_{\theta}\) in \(\theta(x_{i})\) and \(\phi(x_{j})\) to perform weight transformation on \(x\) to obtain \(x_{\theta}\epsilon R^{batch\times H\times W\times C/2}\), \(x_{\phi}\epsilon R^{batch\times H\times W\times C/2}\), aiming to reduce the number of channels and computation. Use the linear embedding function \(g(x_{j})=W_{g}x_{j}\) for information exchange to obtain \(x_{g}\epsilon R^{batch\times H\times W\times C/2}\);
2: Reshape the output in Step 1 to obtain \(x_{\theta}\epsilon R^{batch\times HW\times C/2}\), \(x_{\phi}\epsilon R^{batch\times HW\times C/2}\);
3: According to embedded Gaussian \(f(x_{i},x_{j})=\epsilon^{\beta(x_{i})^{T}\phi(x_{j})}\), after transposing \(x_{\theta}\) in Step 2, the similarity is calculated by matrix multiplication with \(x_{\phi}\) to obtain \(x\epsilon R^{batch\times HW\times HW}\);
4: The output in Step 3 is softmax operated in the last dimension; then, perform the reshape operation with \(x_{g}\) after matrix multiplication to obtain \(x\epsilon R^{batch\times H\times W\times C/2}\);
5: A convolutional kernel of size \(1\times 1\) and number C adjusts the output of Step 4 to the size of the channel when it enters the non-local block. 5. YOLOXFPN The specific process of adding weights to the final output feature maps for dark3, dark4, and dark5 in YOLOXFPN is shown in Figure 7.

Figure 6: Non-local block. Equation (6) for the output feature map with added weights is as follows [40]:

\[ \begin{split} x_{out3}&=Conv(\frac{w^{1}_{out3}*x_{in3}+w^ {2}_{out3}*x_{dim3}}{w^{1}_{out3}+w^{2}_{out3}+\varepsilon}),\\ x_{out4}&=Conv(\frac{w^{1}_{out4}*x_{in4}+w^{2}_{ out4}*x_{dim4}+w^{3}_{out4}*Resize(x_{out3})}{w^{1}_{out4}+w^{2}_{out4}+w^{3}_{ out4}+\varepsilon}),\\ x_{out5}&=Conv(\frac{w^{1}_{out5}*x_{in5}+w^{2}_{ out5}*Resize(x_{out4})}{w^{1}_{out5}+w^{2}_{out5}+\varepsilon}).\end{split}  \tag{6}\]

The calculation Equation (7) of the feature map generated in the middle is as follows [40]:

\[ \begin{split} x_{dim4}&=Conv(\frac{w^{1}_{dim4}*x_{ in5}+w^{2}_{dim4}*Resize(x_{in4})}{w^{1}_{dim4}+w^{2}_{dim4}+\varepsilon}),\\ x_{dim3}&=Conv(\frac{w^{1}_{dim3}*Resize(x_{in3})+w^ {2}_{dim3}*x_{dim4}}{w^{1}_{dim3}+w^{2}_{dim3}+\varepsilon}).\end{split}  \tag{7}\]

To ensure that the divisor is not zero, \(\varepsilon=0.0001\). Before adding fusion, it is necessary to ensure that the fused feature maps have the same size. Therefore, is necessary to resize the feature maps, including adjusting the number of channels and the size of the feature maps. ## 5 Performance Analysis

### Experimental Environment and Data Set

Table 1 shows the operating environment of the experiment.