Object detection algorithm based on attention mask fusion

Xiao-xiao DONG; Xiao-hai HE; Xiao-hong WU; Lin-bo QING; Qi-zhi TENG

doi:10.3788/YJYXS20193408.0825

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Object detection algorithm based on attention mask fusion

Image Processing | 更新时间：2020-12-14

- Object detection algorithm based on attention mask fusion
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 8, Pages: 825-833(2019)
- 作者机构：
  
  四川大学电子信息学院, 四川成都 610065
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193408.0825
  CLC： TP391
- Received：20 March 2019，
  
  Accepted：29 April 2019，
  
  Published：05 August 2019
- 稿件说明：
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Xiao-xiao DONG, Xiao-hai HE, Xiao-hong WU, et al. Object detection algorithm based on attention mask fusion[J]. Chinese journal of liquid crystals and displays, 2019, 34(8): 825-833.
DOI：

Xiao-xiao DONG, Xiao-hai HE, Xiao-hong WU, et al. Object detection algorithm based on attention mask fusion[J]. Chinese journal of liquid crystals and displays, 2019, 34(8): 825-833. DOI： 10.3788/YJYXS20193408.0825.

摘要

在计算机视觉任务中，平衡目标检测的精度与速度对于后续的实际应用如目标跟踪和识别起到关键作用。基于此，提出了一种基于注意力掩模融合的目标检测算法。首先，通过VGG网络提取特征，经过初步二分类和回归后得到一系列预选框；然后，将上述预选框输入到特征金字塔结构中，通过构建注意力掩模模块自适应地学习有效特征，同时融合特征金字塔结构与注意力掩模模块得到更具表征性的特征；最后经过多分类和回归得到多尺度的检测结果。在PASCAL VOC 2007和PASCAL VOC 2012数据集上展开了实验，测试集结果显示，在交集并集比（IOU）为0.5的条件下，对于320×320的图片输入，平均精度均值（mAP）分别为81.0%和79.0%，检测速度为60.9 fps。本文算法将注意力信息结合到目标检测中，实现了通用目标检测的精度和速度均衡。

Abstract

In computer vision tasks

balancing the accuracy and speed of object detection plays a significant role in subsequent practical applications such as object tracking and recognition. An object detection algorithm based on attention mask fusion is proposed. Firstly

the VGG network is used to extract features

and a series of preselected boxes are obtained after preliminary regression and binary classification. Then

the preselected boxes are input into the feature pyramid structure

learning effective features adaptively by constructing the attention mask module

and more representational features are gotten by integrating the feature pyramid structure and the attention mask module. Finally

the multiscale detection results are obtained by multiple classification and regression. Experiments are conducted on the data sets of PASCAL VOC 2007 and PASCAL VOC 2012. Test set results show that under the condition that Intersection over Union(IOU) is 0.5

the mean average precision(mAP) for the image input of 320×320 is 81.0% and 79.0% respectively

and the detection speed is 60.9 fps

realizing the balance between precision and speed. In this paper

the attention information is integrated into object detection to achieve the balance of accuracy and speed of generic object detection.

关键词

Keywords

references

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