## 4 Experiments
### Vehicle Detection and Edge Point Location  
The convolutional neural network MI\(-\)CNN was constructed using the Keras framework. Within this network architecture, the Region Proposal Network (RPN) employed a setting where each anchor generated a pool of 70 candidate boxes [28]. These candidates were evaluated against a coincidence threshold of 0.7 for the candidate regions. Subsequent non\(-\)maximum suppression was executed with a threshold value of 0.6. For training purposes, a total of 11,677 images sourced from the two datasets were randomly selected, while the remaining images were dedicated to testing. The test platform used was a laptop computer with an Intel(R) Core i7\(-\)10700 CPU processor and two RTX\(-\)3060 GPUs. The equipment manufacturer is Dell, from Londrak, Texas, USA. The laptop computer was equipped with an Ubuntu 20.04 system. The training process was completed over approximately 23 h with GPU acceleration. Vehicle detection forms the fundamental cornerstone for the ensuing tasks. To assess the efficacy of MI\(-\)CNN, it is rigorously compared against other contemporary state\(-\)of the\(-\)art methodologies as reported in the recent literature. The Precision\(-\)Recall (PR) curves for each method are graphically presented in Figure 8. In these curves, the abscissa corresponds to the Recall, which signifies the ratio between the number of accurately detected vehicles and the overall number of vehicles. On the ordinate, the Precision indicates the proportion of correctly identified vehicles in relation to the total network outputs. Figure 8: PR curves of different methods. The discernible trend in Figure 8 illustrates that the MI\(-\)CNN network boasts a higher detection accuracy when compared to the majority of prevailing vehicle detection methods. Its performance is second to the MANTA method [23], which integrates an iterative Region Proposal Network (RPN) with prior database information. This observation substantiates that the streamlined basic feature extraction network and the incorporation of the supplementary edge point localization task do not impede the object detection prowess within the dataset. Conversely, they enhance the network's capacity to generalize effectively across diverse scenarios, resulting in improved overall detection performance. The mean Average Precision (mAP) achieved by the MI\(-\)CNN network stands at 89.9%. This figure surpasses the mAP of RSN101 by approximately 1.3%, based on the original ResNet\(-\)101 network. The distinctive strength of MI\(-\)CNN emanates from its adept fusion of rich textures and comprehensive semantic information. Through the amalgamation of shared feature maps across various levels and scales, the network is adept at capturing both intricate details and holistic context. Furthermore, the accuracy of candidate vehicle boxes can be further elevated through subsequent iterations and the optimization of the region of interest (ROI) area. Beyond the 2D detection of vehicles, a paramount aspect is the accurate positioning of edge feature points. These points bear a direct correlation to the precision of vehicle distance measurements. In order to provide a more lucid understanding of the impact of edge point positioning on vehicle distance measurement, two distinct normalized measurement indices are formulated: the Vehicle Measurement Index (_VMI_) and the Height Measurement Index (_HMI_). _VMI_ and _HMI_ represent the absolute difference between 1 and the normalized ratio of the predicted and actual coordinates. Their calculation is as follows:  
\[ \begin{cases}VMI=\left|1-\frac{dy^{t}}{dy^{t}}\right|\\ HMI=\left|1-\frac{dy^{t}}{dy^{t}}\right|\end{cases}  \tag{19}\]  
where \(dy^{t}\) and \(dy^{t}\) represent the longitudinal distance between the optical axis center and the edge point coordinates from network prediction and reality, respectively. \(w^{t}\) and \(w^{p}\) represent the predicted and labeled vehicle width between left and right edge points on the same layer.