摘要
针对小目标检测提出了一种基于特征金字塔网络改进的算法。通过引入预测优化模块,并结合感兴趣区域的上下文信息,使得特征信息具有更强的稳健性,同时通过内部级联的多阈值预测网络进行预测,最终实现多尺度多阶段的预测,在保证网络参数基本不变的前提下准确率得到提升。实验结果表明,经标准数据集VOC07+12训练后,所提算法在VOC2007测试中的准确率达到80.9%,具有很好的检测性能。
An improved algorithm based on feature pyramid networks is proposed for small target detection.A prediction optimization module is introduced,which is combined with the context information of the region of interest to make the feature information more robust,multi-threshold prediction networks with internal cascade are predicted,and the multi-scale and multi-stage prediction is realized finally.On the premise that the network parameters are basically unchanged,the accuracy is further improved.The experimental results show that the accuracy of the proposed algorithm reaches 80.9%in the VOC2007 test after the training of the standard data set VOC07+12,which has good detection performance.
作者
陈景明
金杰
王伟锋
Chen Jingming;Jin Jie;Wang Weifeng(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第21期157-162,共6页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61571320)
关键词
机器视觉
特征金字塔
目标检测
多尺度检测
级联网络
machine vision
feature pyramid
object detection
multi-scale detection
cascade network
