Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection  
[PERSON]  
1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China 1, [PERSON]  
1 School of Information Science and Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China 1, [PERSON]  
1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China 1, [PERSON]  
1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China 1, [PERSON]  
2 School of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China 2 and [PERSON]  
3 School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China 3,4  
important to promote urban economic development. ITS technological development includes two aspects: vehicle and vision [2; 3]. As an important part of the ITS, an auxiliary driving system uses machine vision technology to collect an image of the traffic environment and information pertaining to the preceding vehicle. It can also be used to deal with noise pollution and identify the region of interest (ROI). Therefore, in an emergency, it can activate an alarm quickly and automatically [4]. In the realm of complex and diverse traffic accidents, traffic accidents caused by rear-end vehicle collisions account for a large proportion. If the driver has access to road information before the rear-end accident occurs, they can interpret the driving intention of the preceding vehicle and respond rapidly to prevent a rear-end accident [5; 6]. The traditional automobile intelligent collision avoidance system ignores the comprehensive influence of the driver, the vehicle condition and the road traffic environment on the driving safety state. Therefore, it cannot adapt to the complicated and varied road traffic environment. In recent years, information fusion and situational awareness theory has been introduced into the field of traffic safety to monitor the safety of traffic in complex road traffic environments. The application of Internet of Things (IoT) technology in the automotive and transportation sectors forms an independent network system. It mainly includes sensor technology and information integration, open intelligent vehicle terminal and voice recognition technology. Service terminal computing technology and communication technology are also included in it. The goals of the study are to analyze and predict the driving intentions of the preceding vehicle, with the focus on taillight semantic recognition. This system uses image processing-related methods to achieve image de-noising, filtering and grayscale conversion. It converts images in Red-Green-Blue (RGB) to other color spaces for further research and analysis. Convolutional neural network (CNN) is used to realize the feature extraction of vehicle. After the vehicle detection is completed, the system implements taillight detection and light recognition based on dynamic thresholds and image histogram features in the color space. The system uses the Faster Region CNN (RCNN) to identify vehicles on the road. The Faster RCNN can use the training set images to achieve faster training and higher detection and identification rates. CNN is constructed and trained by manually marking the ROI on the image dataset. The test set is then used to test the performance of CNN, and the results show that the algorithm can achieve high precision. The system converts images from RGB color space to Hue-Saturation-Value (HSV) color space. This achieves a more precise segmentation of taillights and solves the difficulty of detecting red taillights in RGB space. It extracts taillight pairs by using features of a histogram of oriented gradients, position and color correlation, and realizes taillight state detection using the histogram features of the taillight area image.