计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 275-280.doi: 10.11896/jsjkx.200900149
牛康力, 谌雨章, 张龚平, 谭前程, 王绎冲, 罗美琪
NIU Kang-li, CHEN Yu-zhang, ZHANG Gong-ping, TAN Qian-cheng, WANG Yi-chong, LUO Mei-qi
摘要: 随着智慧城市概念的普及,交通道路智能化管理已成为学者关注的热点。针对道路的车流量统计问题,文中基于深度学习方法,提出了基于残差网络的无人机航拍车流量监测算法,该算法引入了全连接的多尺度残差学习分块(FMRB),在解决梯度弥散现象的同时使得图像特征能够被更好地提取和学习。现有的车辆检测算法准确率较低,且大多数仅能对车辆进行检测,不能对车流量进行统计。文章结合视频帧估计方法,实现了车流量的实时监测与统计。在车辆检测性能上将所提算法与SSD,YOLOv2,YOLOv3算法进行对比,结果表明,在自建数据集训练的条件下,所提算法引入多尺度残差学习分块(FMRB)对遥感图像进行车辆识别,能够取得更高的识别精度;在实地车流量监测中,所得结果误检率小于1%,具有较强的实用效果。
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