Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System

Abstract

The vehicle-road collaborative information interaction system is an emerging technology system that realizes the sharing of information between vehicles, vehicles and roads between traffic road information, and driving vehicle information. It is of positive significance for improving the urban transportation construction system and promoting urban economic development. This paper conducts intelligent research on the deep learning recognition method based on the vehicle-road collaborative information interaction system. First, this article comprehensively expounds the concept of the vehicle-road collaborative information interaction system and then introduces the specific components, functions, and applications of the system structure. Then, this article researches on deep learning recognition methods and introduces three deep learning recognition methods. They are background extraction method, YOLOv2 method, and DeepSORT method. Finally, this paper conducts simulation comparison experiments between deep learning algorithms and traditional algorithms. It evaluates the feasibility of the algorithm in the vehicle-road collaborative information interaction system in three aspects: vehicle target detection, vehicle flow identification, and emergency decision-making. The experimental results show that the value of the intersection ratio of vehicle target detection in the deep learning recognition method is 8.66% higher than that of the traditional algorithm, the recall rate is 7% higher than that of the traditional algorithm, and the vehicle flow recognition accuracy is 1.8% higher than that of the traditional algorithm. The early warning time in emergency decision-making is also shorter than that of traditional algorithms, which shows the unique superiority and feasibility of deep learning algorithms in the vehicle-road collaborative information interaction system.

Detection of Cotton Seed Damage Based on Improved YOLOv5

“…Among these, accuracy and recall are two important indicators used to evaluate the performance of the YOLOv5 object-detection system. The accuracy rate refers to how many of the objects detected by the object detection system are correct, and the recall rate refers to the probability that the real object is detected [31]. In general, the higher the correct rate, the higher the recall rate, and the better the performance of the system.…”

PDC: Pearl Detection with a Counter Based on Deep Learning

“…Balakrishnan presented the Faster RCNN ResNet101 algorithm, which was used to detect the number of false ceilings [47]. A deep learning recognition and counting method for the vehicle-road collaborative information was conducted by Hongbin Jiao [48]. Zishuo Huang and Qinyou Hu proposed an improved single shot multibox detector and DeepSORT algorithms and performed numerical experiments, indicating that the ship counting system is more accurate [49].…”

Retracted: Intelligent Research Based on Deep Learning Recognition Method in Vehicle‐Road Cooperative Information Interaction System

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”