# 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
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **Size** & **Params. (M)** & **FPS** & **mAP\({}_{\text{50}}\)** & **mAP\({}_{\text{@50}}\):5.95** \\ \hline EfficientDet-D1 [40] & Efficient-B1 & 640 & 6.56 & 13 & 72.7 & 51.3 \\ \hline YOLOv3 [45] & Efficient-B1 & 416 & 41.1 & 25 & 78.0 & 44.5 \\ \hline Retinanet [46] & ResNet50 & 600 & 37.97 & 34 & 78.9 & 51.5 \\ \hline Centernet [47] & ResNet50 & 512 & 32.67 & 62 & 76.5 & 48.7 \\ \hline YOLOv5-S [13] & CSPDarknet53 & 640 & 7.3 & 90 & 80.9 & 57.7 \\ \hline YOLOX-S [11] & CSPDarknet53 & 640 & 9.0 & 63 & 81.9 & 60.4 \\ \hline YOLOX-S\({}^{1}\) & Improved CSPDarknet53 & 640 & 8.75 & 51 & 83.1 & 61.6 \\ \hline YOLOX-S\({}^{2}\) & Improved CSPDarknet53 & 640 & 8.92 & 48 & 82.9 & 61.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of each algorithm on the PASCAL VOC 2007 test dataset. The number of floating-point operations (FLOPs) can be used to measure the computational complexity of the model. Since the computation of FLOPs is related to the input image size, the same image size is taken for each model in the experiments to compare the complexity of the model. Figure 11 shows the computational complexity of the model at an image size of 640 \(\times\) 640. As shown in Figure 11, the computational complexity of YOLOX-S\({}^{1}\) remains equal to the original model, and although the model with lower model complexity is simpler, the accuracy of detection is lower. Combining the above experimental results, it is proven that after the improvement based on the original model YOLOX-S, YOLOX-S\({}^{1}\) improves the accuracy of the model without adding any model weights or increasing the model complexity, so the YOLOX-S\({}^{1}\) model proposed in this paper is more effective than the existing advanced models. #### 5.3.3 Experimental Results on TACO Dataset and NEU-CLS Dataset

In order to verify the generalization ability of the improved model YOLOX-S\({}^{1}\), the base model YOLOX-S and the model YOLOX-S\({}^{1}\) were trained on datasets in two different scenarios. Tables 6 and 7 respectively show the experimental results of the two models on different data sets. Considering the uneven distribution of objects in the TACO dataset, only the garbage objects with a sample size greater than 200 are selected for the experiments. In the experiment, considering the problem of uneven object distribution in the TACO dataset, only garbage objects with a sample number of more than 200 were selected for the experiment, with a total of eight categories and 1086 images, including clear plastic bottle, plastic bottle cap, drink can, other plastic, plastic film, other plastic wrapper, unlabeled litter, and cigarette. The TACO dataset was divided into a training set and a verification set at a 9:1 ratio with no test set, and the experimental image size was 640 \(\times\) 640. The NEU-CLS dataset was divided into a training set and a test set according to a 9:1 ratio; the separated training set was divided at a 9:1 ratio into a training set and a validation set, and the experimental image size was 320 \(\times\) 320.

baseline YOLOX-S, proving that YOLOX-S\({}^{1}\) is more adaptable to more complex scenarios. Combined with the visualization in Figure 12, it can be seen that although the TACO dataset has a dark background and some object categories are not easily distinguished, YOLOX-S\({}^{1}\) still performs well on the TACO dataset. Analyzing Table 7, YOLOX-S\({}^{1}\) improved the average accuracy over YOLOX-S on the NEU-CLS dataset by 2.8%. Although YOLOX-S\({}^{1}\) did not perform as well as YOLOX-S in the category rolled in scale, it performed better in the remaining five categories, especially in the categories crazing and pitted surface, where accuracy improved by 5.6% and 5.1%, respectively.