激光与光电子学进展, 2019, 56 (23): 231008, 网络出版: 2019-11-27   

基于改进的特征提取网络的目标检测算法 下载: 953次

Object Detection Algorithm Based on Improved Feature Extraction Network
作者单位
天津大学电气自动化与信息工程学院, 天津 300072
摘要
针对目标检测准确率低,物体位置不精准的缺点,设计了一种基于改进的特征提取网络的目标检测算法。首先将训练集进行数据增强;其次设计了一种双通道网络,用于目标检测算法Faster R-CNN的特征提取;最后在算法的预测部分,对非极大值抑制(NMS)机制进行了改进,并采用加权平均方法获取存在多个相近的预测框的位置。在VOC 2007和VOC 2012数据库上进行实验,表明本文算法比经典的目标检测算法效果要好,准确率达到79.1%,提升了3%~4%,验证了本文算法的有效性。
Abstract
In this study, an object detection algorithm is designed based on an improved feature extraction network to solve the shortcomings of low object detection accuracy and inaccurate object position detection. Initially, the training set is enhanced; subsequently, a two-path network is designed for usage in feature extraction of the Faster R-CNN algorithm; finally, the non-maximum suppression mechanism is improved in the prediction part of the algorithm, and the weighted averaging method is adopted for obtaining the positions of multiple similar prediction boxes. The experiments conducted using the VOC 2007 and VOC 2012 databases denote that the proposed algorithm outperforms the classical object detection algorithm, with an accuracy rate of 79.1% and an improvement of 3%-4%. Thus, the effectiveness of the algorithm is verified.

乔婷, 苏寒松, 刘高华, 王萌. 基于改进的特征提取网络的目标检测算法[J]. 激光与光电子学进展, 2019, 56(23): 231008. Ting Qiao, Hansong Su, Gaohua Liu, Meng Wang. Object Detection Algorithm Based on Improved Feature Extraction Network[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231008.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!