# 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
In the future, the characteristics of rolled in scale will be deeply analyzed and YOLOX-S\({}^{1}\) will be further studied to enrich the texture information of YOLOX-S\({}^{1}\) and improve its accuracy when detecting similar rolled in scale categories. Figure 13 shows the detection effect of YOLOX-S\({}^{1}\) for the same defect when the background dimness is inconsistent, further verifying that the improved YOLOX-S\({}^{1}\) model is more adaptable to different scenes. \begin{table}
\begin{tabular}{c c c} \hline
**TACO** & **YOLOX-S** & **YOLOX-S\({}^{1}\)** \\ \hline clear plastic bottle & 77.0 & 87.0 \\ \hline plastic bottle cap & 60.4 & 72.0 \\ \hline drink can & 75.9 & 79.6 \\ \hline other plastic & 34.0 & 35.4 \\ \hline plastic film & 46.3 & 55.1 \\ \hline other plastic wrapper & 62.6 & 63.9 \\ \hline unlabeled litter & 16.7 & 20.2 \\ \hline cigarette & 15.4 & 26.4 \\ \hline \(mAP_{50}\) & 48.5 & 54.9 \\ \hline \(mAP_{@50:5.95}\) & 32.2 & 37.0 \\ \hline \end{tabular}
\end{table} 
Table 6: Model detection results on the TACO dataset. \begin{table}
\begin{tabular}{c c c} \hline
**NEU-CLS** & **YOLOX-S** & **YOLOX-S\({}^{1}\)** \\ \hline crazing & 34.4 & 40.0 \\ \hline inclusion & 71.3 & 74.0 \\ \hline patches & 82.7 & 89.4 \\ \hline pitted surface & 58.9 & 64.0 \\ \hline rolled in scale & 56.7 & 52.6 \\ \hline scratches & 84.8 & 85.5 \\ \hline \(mAP_{50}\) & 64.8 & 67.6 \\ \hline \(mAP_{@50:5.95}\) & 31.1 & 33.6 \\ \hline \end{tabular}
\end{table} 
Table 7: Model detection results on the NEU-CLS dataset. ### Engineering Applications

In the field of intelligent transportation, the detection and tracking of traffic violations is an important application scenario for object detection, that is, using object detection to find illegal motor vehicles in the motorway, vehicles privately occupying the emergency lane, toll station evasion phenomena, suspicious vehicles, and other traffic phenomena to assist public security and traffic control departments. Using the appearance of damaged suspicious vehicles for real-time detection will effectively improve the efficiency of public security while reducing the probability of security accidents. Figure 14 shows types of vehicle damage, including scratching, denting, cracking, creasing, piercing, broken lights, broken windows, and other damaging situations; the pictures are downloaded from the network. Figure 12: Visualization results of YOLOX-S\({}^{1}\) on the TACO dataset. Figure 13: Visualization results of YOLOX-S\({}^{1}\) on the NEU-CLS dataset. However, in practice, the same type of damage is complex and the damage range cannot be predicted in advance, so it is not realistic to use an anchor-based target detector for detection; therefore, as shown in Figure 15, localized damaged vehicle images are collected and used as the training set, and the model in this paper is used for training to pre-determine the vehicle damage category, in addition to predicting the damaged bounding box coordinates and the location of the damaged parts in the body of the output (front, rear, left, right). It then sets the damage threshold according to the degree of vehicle dent, scratch range, broken area and other preliminary judgment of the degree of damage, while according to the set threshold, the degree of damage is classified as light damage, moderate damage, or heavy damage. In addition, the positioned damage area map is saved. In practice, the damage to vehicle parts is complex and varied, and there is a \"coexistence\" situation, i.e., the same area can be scratched and dented, which makes detection

Figure 14: Vehicle exterior damage partial pictures. Figure 15: Damaged area inspection process. difficult. This problem is similar to object overlap in the object detection. Therefore, in the future, methods to solve object overlap can be investigated to improve the detection accuracy of this model in scenes with complex shapes and overlapping objects.