Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Cheng-Jian Lin,Jyun-Yu Jhang

doi:10.1109/access.2022.3147866

Abstract

With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

Highlights

Road traffic monitoring is an important research topic
A novel intelligent traffic-monitoring system combining a YOLOv4-tiny model and counting method was proposed for traffic volume statistics and vehicle type classification
Compared with the current state-of-the-art object detection methods (Retinanet, single-shot multibox detector (SSD), YOLOv4, and YOLOv4 tiny), the proposed you only look once (YOLO)-convolutional fuzzy neural network (CFNN) and YOLO-VCFNN have a high mean average precision (mAP) rate, accurate counting accuracy, and real-time vehicle counting and classification ability

Summary

Introduction

Road traffic monitoring is an important research topic. By analyzing the types of vehicles and traffic flow on the road, current traffic conditions can be understood, and actionable information can be provided to traffic management agencies. This information can help these agencies to make decisions that improve people’s quality of life. If large trucks often use a certain road, roadside warnings can be installed to alert drivers and reduce traffic accidents. The abovementioned applications all rely on information collected by a road monitoring system for analysis. To obtain information on passing vehicles, many researchers have used different methods to achieve vehicle detection and classification

Methods

Results

Conclusion