# 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
\[FPS=\frac{1}{Latency}.\] (9) where _latency_ is the computation time of the model, i.e., the average time it takes for an image to be computed by the model;
* Model parameters and model computational complexity: This metric is found by inputting the same batch of images to the model and using the model weight calculation package profile in the Pytorch framework to calculate the parameter amount and calculation complexity of the model. The former unit is MB, and the larger the latter value, the more complex the model. ### Experimental Results and Analysis

#### 5.3.1 Ablation Experiments on the PASCAL VOC Dataset

In the experiments, considering that the reduction of the parameter amount in the backbone network affects the accuracy of the model, to verify the feasibility of cheap convolution, the feature map channel is boosted using cheap convolution in the YOLOX-S model and the feasibility of cheap convolution is verified by the accuracy comparison in Table 2. Meanwhile, to verify the feasibility of the local image feature extraction module, Table 2 also shows the variation of model accuracy, parameter size, and model computation on the validation set after adding the local feature extraction module. In addition, the size of the images in the ablation experiments were all 640 \(\times\) 640. In the encoder part of the model, as shown in Figure 4, the 3 \(\times\) 3 convolution in the backbone network part in YOLOX-S is replaced with the cheap convolution GhostModuleConv in dark2, dark3, dark4, and dark5 for the upscaling operation on the feature maps. Take dark3 as an example where the input is \(xcR^{64\times 160\times 160}\), the output is \(xcR^{128\times 80\times 80}\); the filter taken in the YOLOX model has a convolution kernel of size 3 \(\times\) 3, step size 2, and number 128; and the number of parameters needed is 64 \(\times\) 128 \(\times\) 3 \(\times\) 3 = 73,728. After using GhostModuleConv, the number of parameters of convolution is (64 \(\times\) 64 \(\times\) 3 \(\times\) 3) + (64 \(\times\) 64 \(\times\) 1 \(\times\) 1) = 40,960; the parameters are reduced by nearly half. Table 2 shows that after replacing the convolution of raised channels used in the backbone network for feature extraction with a convolution method with fewer parameters, generating half of the desired input channels with 3 \(\times\) 3 convolution, and generating the other half with depthwise convolution, there is no loss in the accuracy of the model based on a 0.83 M reduction in the convolution parameters but, on the contrary, the accuracy of the model is improved by 1.13%, while the complexity of the model is also reduced. The feasibility of using inexpensive convolution for feature extraction is verified. In the experiments, considering that the feature maps close to the input layer are noisy and significant detailed information of the feature maps in the deep layer is lost, the local-feature extractor module is added to dark3, as shown in Table 2. After adding the local-feature extractor module to the model, the accuracy of the model is further improved, and the feasibility of local image feature extraction after blocking the image is verified. To verify the feasibility of the global feature extraction module non-local block, after using GhostModuleConv and the local-feature extractor in the backbone network, we \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP(\%)** & **Parameters** & **GFLOPs** \\ \hline YOLOX-S [11] & 76.75 & 9.0 M & 26.8 \\ \hline \(+\)GhostModuleConv & 77.88\({}^{\dagger 1,13}\) & 8.17 M & 24.82 \\ \hline \(+\)Local-feature extractor & 78.06\({}^{\dagger 1,13}\) & 8.17 M & 24.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model accuracy before and after adding GhostModuleConv. add the non-local block to the model to verify the feasibility of the idea. If the non-local block is added to the lower network, it will introduce complicated computation due to the high resolution of the shallow feature map.