摘要
针对城市交通流量和交通违法行为不断增长的问题,设计了一种基于图像处理技术的交通违法非现场执法自动化控制系统。基于先进的卷积神经网络,该系统可用于分析交通监控摄像头捕捉到的图像,实现对交通违法行为的自动检测和精准识别。同时,引入了高效的图像质量评估算法,选取清晰的违法图像作为执法依据,显著减轻了人工审核的负担,提升了交通违规检测的智能化程度。
A traffic violation off-site law enforcement automation control system based on image processing technology has been designed to address the growing problem of urban traffic flow and traffic violations.Based on advanced convolutional neural networks,this system can be used to analyze the images captured by traffic surveillance cameras,achieving automatic detection and accurate recognition of traffic violations.At the same time,an efficient image quality evaluation algorithm has been introduced,selecting clear illegal images as law enforcement basis,significantly reducing the burden of manual review and improving the intelligence level of traffic violation detection.
作者
黄杰
HUANG Jie(Zhongtian Technology(Qingyuan)Co.,Ltd.,Qingyuan,Guangdong 511800,China)
出处
《自动化应用》
2024年第14期186-188,共3页
Automation Application
基金
基于云计算网络安全治理超限超载的关键应用技术的研究(广科成登字[2023]B0119号)
市级科技局科研项目“智慧交通云计算网络安全智能监测预警技术应用”(2023KJJ014)。
关键词
图像处理
交通违法
非现场执法
自动化控制
image processing
traffic violations
off-site law enforcement
automated control
