## 3 System Function Realization
### YOLOv8 Target Detection  
The comparison of different target detection algorithms is shown in Table 4. Compared with other algorithms, YOLOv8 has significant advantages in terms of accuracy, real-time performance, and robustness [13; 14; 25]. YOLOv8, an object detection model known for its accuracy and fast inference, consists of three key components: Backbone, Neck, and Head. The Backbone extracts features using convolutional and deconvolutional layers, with residual connections and bottleneck structures to optimize network size and performance. It uses the C2f module, which has fewer parameters and better feature extraction than YOLOv5's C3 module [26]. The Neck module enhances image representation by merging features from different stages of the Backbone, including an SPPF module, a PAA module, and two PAN modules. The Head module handles object identification and classification, with a detection head made up of convolutional and deconvolutional layers using global average pooling to produce the classification results. The network's structure is illustrated in Figure 6.  
Dataset acquisition is crucial for object detection. The intelligent detection system mainly identifies vehicles using a dataset obtained from the open-source UA-DETRAC. To enhance training accuracy, data augmentation techniques are essential. Initially, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristic** & **YOLOv8** & **YOLOv5** & **Faster R-CNN** & **DETR** & **SSD** \\ \hline FPS & 40 FPS & 33.3 FPS & 5–10 FPS & \textless{}1 FPS & 22.22 FPS \\ \hline [EMAIL_ADDRESS] & 71.5\% & 50.5\% & 75–80\% & 94\% & 74\% \\ \hline \multirow{3}{*}{Robustness} & Excellent performance in complex scenes and small targets & Limitation in small target detection & Excellent performance in small targets but slow & Excellent performance in complex scenes but slow & Excellent performance in complex scenes but slow & Excellent performance in complex scenes but general adaptability to small targets \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of target detection algorithms. training data undergo custom augmentation. Subsequently, the input data are enhanced with color jitter, random horizontal flipping, and random scaling by 10%. The image, regarded as a collection of pixels, can be expressed as a matrix. YOLOv8 analyzes the input image, capturing its features through feature extraction technology. After calculations, the program identifies objects and provides their locations and labels. This process is realized by the trained weights, which are the core of the algorithm. These weights form the basis for image reasoning and target identification, equivalent to the criteria and standards used in the recognition process. Figure 7a illustrates the model's performance in target monitoring in terms of accuracy and recall at various thresholds. Assuming that there are only two types of targets to be classified, positive and negative, the following four metrics are used: (1) _TP_ (true positive): the number of positive examples that are correctly identified as positive; (2) _FP_ (false positive): the number of negative examples that are incorrectly identified as positive; (3) _TN_ (true negative): the number of negative examples that are correctly identified as negative; (4) _FN_ (false negative): the number of positive examples that are incorrectly identified as negative. _Precision_ measures the accuracy of model predictions, indicating the proportion of true positives within the predicted positives. \[Precision=\frac{TP}{TP+FP} \tag{3}\]  
_Recall_ indicates the proportion of actual positives correctly identified by the model, reflecting its comprehensiveness. \[Recall=\frac{TP}{TP+FN} \tag{4}\]  
Average precision (AP), the area under the precision-recall curve, measures the model's overall performance across all thresholds and is a key indicator of its effectiveness. A high AP value indicates that the model maintains high accuracy and recall across different thresholds, enhancing its application effectiveness.