中国计量大学学报2018，Vol.29Issue(4)：393-397,5.DOI:10.3969/j.issn.2096-2835.2018.04.008
基于多尺度特征融合的Faster-RCNN道路目标检测
Road object detection based on multi-scale merged feature Faster-RCNN
摘要
Abstract
Road object detection plays a vital role in intelligent city construction;and Faster-RCNN is one of the main stream object detection algorithms.The paper proposed a concatenated feature pyramid network layer on the original Faster-RCNN feature extraction layer and substituted the cross entropy loss function with the focal loss function.The concatenated feature pyramid network could extract the enriched feature maps that were more robust and generalized to diverse situations.The adopted focal loss function could alleviate the sample inhomogeneity in the detected picture.The algorithm was verified by using open KITTI datasets.The result shows that the updated Faster-RCNN algorithm can improve detection precision.关键词
目标检测/特征融合/卷积神经网络/Faster-RCNN算法Key words
object detection/merged feature/CNN/Faster-RCNN分类
信息技术与安全科学引用本文复制引用
陈飞,章东平..基于多尺度特征融合的Faster-RCNN道路目标检测[J].中国计量大学学报,2018,29(4):393-397,5.基金项目
浙江省自然科学基金项目(No.LY15F020021) （No.LY15F020021）
浙江省公益技术应用研究计划项目(No.2016C31079) （No.2016C31079）
