## Vehicle Detection  
### Faster RCNN Model  
The traditional method of detecting taillights is to detect taillights directly in the image using feature matching or image processing techniques. This method will cause more interference in the image, which is not conducive to the detection of the tail light. Therefore, this paper introduces the technology of vehicle detection. After the vehicle detection is completed, the taillight detection can effectively reduce the interference in the image.  
In the process of vehicle detection, there may be image quality and angle, as well as illumination and other factors, resulting in image quality degradation or target scale deformation. The vehicle detection method based on Faster RCNN can effectively eliminate interference and has relatively strong generalization ability. Neural networks have always been one of the most popular research topics in the field of artificial intelligence. It models the process of information processing by abstractly simulating the human brain and establishes probabilities or classification models based on different methods of neuron connections.  
In recent years, the research on the structure of the CNN has been deepening. The CNN is widely used in various fields, such as behavior recognition, pedestrian detection, human posture recognition, and so on [19; 20]. Recently, the CNN has been developed toward artificial intelligence, such as speech recognition and natural language processing, and so on.  
According to current research and investigation, CNN can learn high-level image group features from a massive amount of image information. AlexNet has achieved excellent results in the field of large-scale image classification. It was designed in 2012 by the winners of ImageNet competitions, [PERSON] and [PERSON] [21]. RCNN based on region feature extraction transforms the target detection problem through the region proposal method with the help of the good feature extraction and classification performance of CNN. This is a milestone in applying the CNN method to the object detection problem [22]. A Fully Convolutional Network (FCN) achieves the pixel-level classification of images, thus resolving the problem of semantics-level image segmentation [23].  
CNN is a powerful feature extraction method. [PERSON] et al. tried to regard object detection as a regression problem, but the detection effect on VOC2007 is average [24]. [PERSON] et al. used the candidate region method to solve the detection problem and obtained a good effect on VOC2007 [25]. Fast RCNN was proposed in 2015, and it mapped candidate regions to the last layer feature map of CNN, which solves the problem of many repeated computations in RCNN and improves the speed of object detection [26]. In the same year, the Faster RCNN was proposed, whose basic structure includes the convolutional layer, the Region Proposal Network (RPN) layer, the ROI pooling layer and the classification layer [27], as shown in Figure 1. It further improves the speed and accuracy of object detection by using the shared features of RPN and Fast RCNN. Faster RCNN, one of the mainstream frameworks for object detection, was built in and has been used since late 2015, although Mask RCNN and other improved frameworks have been developed since then. The basic structure of Mask RCNN has not changed much and Faster RCNN still has the best accuracy in practical applications.