# 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
As can be seen from Figure 8, the pixel sizes of boat, sheep, potted plant, car, and bottle in the images are smaller compared to other categories, and the average relative area does not exceed 5%, while the accuracy of the categories is better on YOLO-S\({}^{1}\), except for the category potted plant, which has slightly lower accuracy. In summary, the effectiveness of the feature extraction method for local images is verified; i.e., after the chopping operation of the feature map, the same feature map no longer shares the same convolutional kernel, but uses more convolutional kernels to do feature extraction for different parts of the same feature map, so that each small piece of the divided feature map can obtain the focus of independent convolution, thereby facilitating the network to extract more detailed information and improving the detection performance of the object. Analyzing the data in Table 4, the detection accuracy of all three models for the category potted plant is not high. From the images in the dataset, it can be observed that the category potted plant has more shapes; meanwhile, the number of the related image is low, and this affects the detection performance of the model. In the future, how to handle and detect categories of objects with variable and irregular shapes will also be a significant focus of research. Combining the data in Figure 8 and Table 4, among the classes with an average relative area between 5% and 10%, the categories person, bird, cow, chair, and tv monitor all have considerable point increases, notably, 3.3% for the category bird and 2.3% for tv monitor, further verifying the effectiveness of the algorithm in this paper. Comparing YOLOX-S\({}^{1}\) and YOLOX-S\({}^{2}\), the category detection results do not differ much and the performance of each object category accuracy is better in YOLOX-S\({}^{1}\), thus proving that when fusing the network of the output layer, the output feature map does not need to use more convolutional kernels for feature extraction because it has already experienced many layers of convolutional layers. If using \begin{table}
\begin{tabular}{c c c c} \hline \hline
**VOC 2007 Test** & **YOLOX-S** & **YOLOX-S\({}^{1}\)** & **YOLOX-S\({}^{2}\)** \\ \hline aeroplane & 89.2 & 89.7 & 89.5 \\ \hline bicycle & 89.1 & 89.8 & 88.5 \\ \hline bird & 79.0 & 82.3 & 82.1 \\ \hline boat & 75.9 & 77.7 & 77.4 \\ \hline bottle & 73.0 & 73.5 & 72.2 \\ \hline bus & 87.3 & 87.8 & 86.6 \\ \hline car & 89.5 & 89.5 & 89.7 \\ \hline cat & 86.6 & 87.3 & 87.9 \\ \hline chair & 67.1 & 67.9 & 69.8 \\ \hline cow & 87.0 & 88.9 & 87.5 \\ \hline diningtable & 78.9 & 79.2 & 80.6 \\ \hline dog & 84.9 & 86.1 & 85.2 \\ \hline horse & 88.3 & 90.0 & 89.2 \\ \hline motorbike & 88.0 & 89.0 & 87.8 \\ \hline person & 86.6 & 86.7 & 86.8 \\ \hline pottedplant & 62.0 & 61.1 & 61.6 \\ \hline sheep & 81.9 & 85.1 & 85.3 \\ \hline sofa & 78.4 & 79.6 & 79.7 \\ \hline train & 84.7 & 86.3 & 86.9 \\ \hline tvmonitor & 81.4 & 83.7 & 83.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision values for all classes on the PASCAL VOC 2007 test set for each model. \(1\times 1\) convolution for channel dimensionality to achieve dimensionally consistent fusion prerequisites, more parameters will be produced, and reusing the already generated feature maps reduces the use of convolutional parameters. Comparing the results of YOLOX-S\({}^{1}\) and YOLOX-S\({}^{2}\), the feasibility of feature reuse in YOLOX-S\({}^{1}\) is further proven, which also conforms to the verification in GhostNet; as the network becomes deeper, it generates more redundant feature maps that can be reused. In addition to the smaller percentage of categories that have improved in accuracy, others have also increased in points. Figure 9 shows the visualization results of YOLOX-S and YOLOX-S\({}^{1}\). It can be seen that there are false detections, missed detections, and inaccurate localizations in YOLOX-S. Therefore, the visualization results further indicate that YOLOX-S\({}^{1}\) performs better.