计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 264-268.doi: 10.11896/jsjkx.201200196
李亚泽, 刘宏哲
LI Ya-ze, LIU Hong-zhe
摘要： 随着智能驾驶领域的发展,人们对目标检测的精度要求越来越高,尤其是针对高速行驶时对距离较远的小目标的检测和低速行驶时对密集目标的检测。在当前的两阶段检测框架的特征融合部分,使用bottom-up的双向融合方法虽然能够更有效地对大目标进行语义信息和位置信息的特征融合,但会给几个或几十个像素的小目标造成很大的信息损失。当检测网络特征融合部分使用top-down的单向融合方法时,则对大目标检测的效果欠佳。为此,文中提出了相邻特征融合(Neighbour Feature Pyramid Network,NFPN)方法、Double RoI(Region of Interest)方法和递归特征金字塔(Recursive Feature Pyramid,RFP)的方法。以Faster RCNN 50为基准,同时使用提出的NFPN,Double RoI和RFP后,在Lisa交通数据集中平均精度(mAP)提升了2.6个百分点。在VOC2007数据集上,以VOC07+12 train数据集为训练集,VOC2007 test为测试集,以Faster RCNN101为基准,同时使用提出的3个模型,mAP提升了6个百分点,同时小、中、大目标的精度也得到提高。
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