计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 301-304.doi: 10.11896/j.issn.1002-137X.2019.06.045
杜晨锡1,2, 严云洋1,2, 刘以安1, 高尚兵2
DU Chen-xi1,2, YAN Yun-yang1,2, LIU Yi-an1, GAO Shang-bing2
摘要： 一般火焰检测方法由于对复杂场景的应变能力较差,因此检测率较低。文中提出了一种基于改进的YOLOv2网络的深度学习火焰检测方法,来自动提取火焰特征;同时,针对特征提取过程中信息丢失的问题,采用聚类选取候选框,以多尺度特征融合的方法融合高层与浅层特征信息,进一步提高了模型的检测率。在Bilkent大学火焰视频数据集上的实验结果表明,该方法的平均正检率达到了98.8%,检测速率达到40帧/s,具有较强的鲁棒性和实时性。
中图分类号:
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