摘要
【目的】车辆检测是城市智能交通研究的重要部分,以交通监控图像作为输入,以运动车辆的检测作为目标,围绕其中的小目标问题、高密集问题以及运动属性问题开展研究。【方法】在无锚框CenteNet的基础上提出一种基于多尺度双源运动感知的检测方法。首先,引入坐标注意力,并融合网络抽象层的多尺度和全局上下文特征,多层次多阶段地补充信息,提高模型对车辆和场景的理解力;其次,借助代表车辆实际运动特征的模糊纹理和代表车辆通用运动特征的光流知识,构建模型对运动车辆的感知能力。【结果】实验数据来源于公共数据集UA-DETRAC,以均值平均精度(mAP)和帧率(FPS)作为精确度和速度评价指标,将文章方法与已有主流方法进行比较,结果表明文章方法的mAP和FPS分别为72.46%和30 frame/s,在对比方法中具有最佳的速度与准确率均衡性。【结论】文章方法能够胜任运动车辆的检测任务。
【Objective】Vehicle detection is critical for urban intelligent transportation.Focusing on small target problems,high-density problems,and motion attribute problems,this study takes traffic surveillance images as input and aims to detect moving vehicles.【Method】Based on the anchor-free CenteNet,a detection method of multi-scale features and dual-source motion perception was proposed.Firstly,coordinate attention was intro-duced to the multi-scale and global context features of the network's abstraction layer,so as to supplement infor-mation in multiple stages and at multiple levels and improve the model's understanding of vehicles and scenes.Secondly,through fuzzy textures representing actual motion features of vehicles and optical flow knowledge rep-resenting general motion features of vehicles,the model's perception ability of moving vehicles was constructed.【Result】The experimental data came from the public dataset UA-DETRAC.The mean average precision(mAP)and frames per second(FPS)were used as the evaluation metrics of accuracy and speed.Experiment results show that the mAP and FPS of the proposed method are 70%and 30 frame/s respectively,which have the best balance between speed and accuracy among other compared methods.【Conclusion】It maintains that the pro-posed method is competent in the task of moving vehicle detection.
作者
李晓晗
刘石坚
刘建华
戴宇晨
邹峥
Li Xiaohan;Liu Shijian;Liu Jianhua;Dai Yuchen;Zou Zheng(Fujian Provincial Key Laboratory of Big Data Mining and Applications,Fuzhou 350118,China;College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China)
出处
《华东交通大学学报》
2024年第4期64-72,共9页
Journal of East China Jiaotong University
基金
国家自然科学基金项目(62172095)
福建省科技厅自然科学基金项目(2022J01932)
福建省教育厅科技项目(JAT210283,JAT220052)
福建省创新资金项目(2022C0022)
湖南省自然科学基金区域联合基金项目(2024JJ7549)。
