计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210600142-7.doi: 10.11896/jsjkx.210600142
顾曦龙, 宫宁生, 胡乾生
GU Xi-long, GONG Ning-sheng, HU Qian-sheng
摘要: 为了能快速、有效地识别视频中的车辆信息,文中结合YOLOv3算法和CNN算法的优点,设计了一种能实时识别车辆多标签信息的算法。首先,利用具有较高识别速度和准确率的YOLOv3实现对视频流中车辆的实时监测和定位。在获得车辆的位置信息后,再将车辆信息传入经过简化与优化的类VGGNet多标签分类网络中,对车辆进行多标签标识。最后将标签信息输出至视频流,得到对视频中车辆的实时多标签识别。文中训练与测试数据集来源为KITTI数据集和通过Bing Image Search API获取的多标签数据集。实验结果证明,所提方法在KITTI数据集上的mAP达到了91.27,多标签平均准确率达到80%以上,视频帧率达到35 fps,在保证实时性的基础上取得了较好的车辆识别和多标签分类效果。
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