# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 2 Related Works
### Vehicle Object Detection  
The detection of vehicles based on computer vision represents a fundamental element of intelligent transportation systems. Conventional computer vision techniques rely on the differentiation of vehicle objects from a fixed background through the analysis of their motion. These methods can be divided into three main categories [30]: background subtraction, which uses differences between the current image and background images [31]; frame differencing, which relies on pixel intensity differences between consecutive images [32]; and optical flow methods, which use the motion direction and speed of pixels in an image sequence [33]. However, these methods are unable to detect vehicle types. In addition, under low-light conditions, it is difficult to extract vehicle edges or detect moving vehicles, resulting in low detection accuracy and affecting the further use of detection results. The effectiveness of conventional vehicle detection methods relies on the quality of the background model; when there are changes in illumination, periodic movements in the background, slow-moving vehicles, or complex application scenarios, these methods do not perform well. With the development of deep learning, many researchers have conducted studies on traffic object detection in complex traffic scenes. [PERSON] et al. proposed DC-SPP-YOLO based on dense connections and spatial pyramid pooling [34]. This model demonstrated excellent performance on the UA-DETRAC vehicle dataset, with a 2.25% improvement in mAP, though the fps decreased by 8.3%. [PERSON] et al. restructured the YOLOv3 backbone network and designed a lightweight traffic object detection algorithm [35]. Although the model is still larger than YOLOv3-tiny, its mAP on the KITTI dataset outperforms both YOLOv3-tiny and YOLOv3. [PERSON] et al. introduced SCD-YOLO [36], which reduces the model's parameters by 44.4% through pruning and knowledge distillation, with only a 0.5% drop in mAP on the BIT-vehicle dataset. [PERSON] et al. improved the YOLOv3-tiny algorithm [37]. To meet real-time requirements, they significantly increased detection speed through model compression and simplification. However, the detection accuracy on their proprietary vehicle detection dataset dropped by 9.1%, and the model's generalization ability decreased, making accurate detection difficult in complex traffic scenarios such as shadows, rain, and fog. These methods are significant for vehicle detection, but they still have the following issues: (1) Although most approaches improve detection accuracy in certain cases, they often involve large model parameters and high computational complexity. (2) Some lightweight models effectively reduce model parameters but fail to achieve a balance between accuracy and speed.  
Compared with the existing methods, this paper simultaneously addresses the challenges of target scale variation and complex background in traffic surveillance by introducing WIoU and introducing attention modules in the backbone network. A compact layer aggregation network is proposed for feature extraction to improve the computational efficiency of the model. This approach achieves a balance between model size and accuracy.