摘要
在当前复杂的道路交通环境下,车辆作为道路交通范畴内的一个重要参与群体,对其有效的监测和监管成为了涉及交通运输安全问题中的一个重要内容。以道路交通车辆目标为研究对象,结合模糊聚类算法在图像处理领域所展现的技术优势,将其进一步扩展到车辆目标检测的实际应用之中,并针对该算法所存在的性能方面的不足之处,提出和设计基于超像素的像素级以及基于群智能的种群级自适应优化方法。实验仿真结果表明,该方法能够有效地提升车辆目标检测的性能,进而为高效精准的车辆目标检测提供理论和方法上的参考依据。
In the current complex road traffic environment,as an important participant group in the field of road traffic,the effective monitoring and supervision of vehicles becomes an important part of the traffic safety issues.In this paper,taking the road traffic vehicle as the research object,and combined with the technical advantages of fuzzy clustering algorithm in the field of image processing,it further extends the method to the practical application of vehicle detection.In view of the performance shortcomings of the method,a super pixel based pixel level and a swarm intelligence based population level adaptive optimization method are proposed and designed.Experimental simulation results show that the proposed method can effectively improve the performance of vehicle detection,and provide theoretical and methodological reference for efficient and accurate vehicle detection.
作者
王璐
WANG Lu(Department of Image and Network Investigation,Zhengzhou Police University,Zhengzhou 450053 China)
出处
《自动化技术与应用》
2024年第7期44-48,共5页
Techniques of Automation and Applications
基金
公安部技术研究计划项目(2019JSYJC25)
河南省科技攻关计划项目(232102240015,222102210198,212102310485)
河南省软科学研究计划项目(232400411112)
中央高校基本科研业务经费项目(2021TJJB KY001,2020TJJBKY001)
河南省高等学校重点科研项目(23A580004)。
关键词
自适应
优化
聚类
车辆检测
道路交通
目标检测
adaptive
optimization
clustering
vehicle detection
road traffic
object detection
