# 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
On the contrary, if the non-local block is added to the deep network, it will not introduce too much computation and the deep feature map integrates the information of the lower feature map. Therefore, due to comprehensive consideration, the non-local block is added to layer 4 and layer 5 (dark4, dark5) of the backbone network in the experiment to enrich the global information of the deep feature maps. Table 3 shows the experimental results, where R.W represents the reuse and weighting operation of the feature maps, \(AP_{\textbf{650}:5:95}\) represents the result of averaging the values of IOU from 50% to 95% in 5% steps on the validation set, and \(AP_{test}\) represents the effect of taking the values of IOU at 50% on the test set for each category of accuracy values. The idea of reusing the feature maps is to use the feature maps that have already been generated. Which layer to select the feature maps from and how to ensure that the existing information of the feature maps is not lost before using them are the key issues considered in the experiments. To follow the idea of not performing any convolution and pooling operations before reusing the feature map, the feature map that is generated should have the same number of channels as the feature map that needs to be raised. Considering that the feature map in the backbone network will have insufficient feature extraction when the feature map is not extracted by convolution in the deeper network, \(x_{in2}\) in Equation (5) is adopted as \(x_{in1}\), which means reusing its feature map. As seen from the data in Table 3, the accuracy of the model varies after adding the non-local block and R.W to different layers. After adding the non-local block to dark5, the accuracy of the model does not improve significantly, but it still performs better than the baseline model YOLOX-S; after adding R.W, the accuracy of the model improves, which verifies the effectiveness of reusing and weighting the feature maps. After adding the non-local b+9 lock to dark4, the accuracy of the model improves significantly, which also confirms that the global information of the deep feature maps is the most abundant. Finally, after adding the non-local block to both dark4 and dark5 in the model and taking the feature map reuse and weighting operation for the feature map, the accuracy of the model is the best in the test set. The comprehensive experimental data verifies the feasibility of the idea. #### 5.3.2 Comparative Experiments on the PASCAL VOC Dataset

The experiment compares YOLOX-S, YOLOX-S\({}^{1}\), and YOLOX-X\({}^{2}\). YOLOX-S model is the baseline model and YOLOX-S\({}^{1}\) is the model after improving it with the ideas proposed in this paper, using YOLOX-S as the base model. Meanwhile, in order to further verify the feasibility of reusing the feature maps, the difference between YOLOX-S\({}^{2}\) and YOLOX-S\({}^{1}\) is that before the final fusion, YOLOX-S\({}^{2}\) takes a \(1\times 1\) convolution to adjust the number of channels of convolution and YOLOX-S\({}^{1}\) takes the method in Equation (5) to adjust the number of channels. The accuracy values of each model in the experiments for each category in the PASCAL VOC 2007 test dataset are given in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dark4** & **Dark5** & **Method** & \(AP_{\textbf{650}:5:95}\)**(\%)** & \(AP_{test}\)**(\%)** & **Parameters** \\ \hline  & ✓ & \(+\)Non-local block & 77.86 & - & 8.75 M \\  & ✓ & \(+\)R.W & 77.98 & 82.5 & 8.49 M \\ \hline ✓ & & \(+\)Non-local block & 78.35 & - & 8.44 M \\  & & \(+\)R.W & 78.33 & 82.6 & 7.70 M \\ \hline ✓ & ✓ & \(+\)Non-local block & - & - & 9.49 M \\  & & \(+\)R.W & 78.23 & 83.1 & 8.75 M \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of model accuracy after adding the non-local block, reusing feature maps, and weighting feature maps. Analyzing Table 4, compared with Baseline YOLOX-S, the accuracy values of model YOLOX-S\({}^{2}\) in all categories have different degrees of improvement.