基于D-LinkNet与形态学计算的高分遥感图像车流量监测  

Traffic Flow Monitoring of High-resolution Remote Sensing Images Based on D-LinkNet and Morphological Computing

作　　者：刘颖 吴凌寻 朱明辉 LIU Ying;WU Lingxun;ZHU Minghui(School of Electronic Information and Engineering,Nanjing Vocational Institute of Transport Technology,Nanjing 211119,China)

机构地区：[1]南京交通职业技术学院电子信息工程学院,南京211119

出　　处：《电讯技术》2024年第5期663-669,共7页Telecommunication Engineering

基　　金：2022年江苏省产学研合作项目(BY2022639);2023年江苏省“333”人才资助项目(712205004);2023—2024年度江苏职业教育研究课题(XHYBLX2023211);南京交通职业技术学院科研基金项目(JZ2105)。

摘　　要：针对城市交通路面车流量监测实时性和准确性高的需求,设计了一种利用高分遥感图像进行道路网自动提取和车辆自动监测的处理方法。综合利用D-LinkNet和形态学计算实现道路区域的二值化语义分割及连贯性、边缘扩展以及平滑性优化,同时将道路信息作为掩码并外溢后进行车辆目标检测,有效避免非道路区域车辆目标干扰。提出采用热力图的形式改进车流量监测方法,可以更直观显示道路拥挤程度。综合利用现有数据集对所提方法进行评价,车辆检测的平均精度达91.7%,道路提取平均交并比达85.3%,可以实现道路车流量的有效监测。According to the requirement of high real-time and accuracy for urban traffic pavement traffic flow monitoring,a processing method for automatic extraction of road network and automatic vehicle monitoring using high-resolution remote sensing images is designed.The D-LinkNet and morphological computing are comprehensively used to realize the binary semantic segmentation and coherence optimization,edge expansion and smoothness optimization of the road area,and the vehicle target detection is carried out after the road information is used as a mask and spilled over,so as to effectively avoid the interference of vehicle targets in non-road areas.It is proposed to improve the traffic flow monitoring method by using heat map,which can show the road congestion degree more intuitively.Through using the existing data set,the average accuracy of vehicle detection is 91.7%,and the average intersection ratio is 85.3%.The results show that the presented method can realize the effective monitoring of road traffic flow.

关 键 词：道路网提取 车流量监测 车辆检测 高分遥感影像 形态学运算 

分 类 号：TN209[电子电信—物理电子学]