南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (2): 231237.doi: 10.13232/j.cnki.jnju.2019.02.008
杨 薇,王洪元*,张 继,张中宝
Yang Wei,Wang Hongyuan*,Zhang Ji,Zhang Zhongbao
摘要: 随着交通愈加发达,道路愈加拥堵,如何实时准确地获取车辆基本信息以便交通部门及时管理特定路段和路口的车辆显得日益重要. 对交通视频中车辆的检测和识别,不仅需要实时检测,还要保证其准确性. 针对实际情况中车辆之间的遮挡、光照的变化、阴影、道路旁树枝的晃动、背景中固定对象的移动等因素严重影响检测与识别的精度的问题,提出基于Faster-RCNN(Faster-Regions with CNN features)的车辆实时检测改进算法. 首先采用k-means算法对KITTI数据集的目标框进行聚类,得到合适的长宽比,并增加一组尺度(642)以适应差异较大的车辆尺寸;然后改进区域提案网络,降低计算量,优化网络结构;最后在训练阶段采用多尺度策略,降低漏检率,提高精确率. 实验结果表明:改进后的车辆检测算法的mAP(mean Average Precision)达到了82.20%,检测速率为每张照片耗时0.03875 s,基本能够满足车辆实时检测的需求.
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