基于注意力掩模融合的目标检测算法

董潇潇; 何小海; 吴晓红; 卿粼波; 滕奇志

doi:10.3788/YJYXS20193408.0825

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基于注意力掩模融合的目标检测算法

图像处理 | 更新时间：2020-12-14

- 基于注意力掩模融合的目标检测算法
- Object detection algorithm based on attention mask fusion
- 液晶与显示 2019年34卷第8期页码：825-833
- 作者机构：
  
  四川大学电子信息学院, 四川成都 610065
- 作者简介：
  
  [ "董潇潇(1994-), 女, 湖北黄冈人, 硕士研究生, 2016年于湖北工业大学获得学士学位, 研究方向:数字图像处理、目标检测与识别。E-mail:alyssa21@163.com" ]
  [ "何小海(1964-), 男, 四川绵阳人, 教授, 博士生导师, 2002年于四川大学获得博士学位, 研究方向:图像处理与网络通信、机器视觉与智能系统等。E-mail:nic5602@scu.edu.cn" ]
- 基金信息：
  
  成都市产业集群协同创新项目(2016-XT00-00015-GX);国家自然科学基金(61871278);Chengdu Industrial Cluster Collaborative Innovation Project(2016-XT00-00015-GX);National Natural Science Foundation of China(61871278)
- DOI：10.3788/YJYXS20193408.0825
  中图分类号： TP391
- 收稿日期：2019-03-20，
  
  录用日期：2019-4-29，
  
  纸质出版日期：2019-08-05
- 稿件说明：
移动端阅览
董潇潇, 何小海, 吴晓红, 等. 基于注意力掩模融合的目标检测算法[J]. 液晶与显示, 2019,34(8):825-833.

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.
董潇潇, 何小海, 吴晓红, 等. 基于注意力掩模融合的目标检测算法[J]. 液晶与显示, 2019,34(8):825-833. DOI： 10.3788/YJYXS20193408.0825.

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

黄继鹏, 史颖欢, 高阳.面向小目标的多尺度Faster-RCNN检测算法[J].计算机研究与发展, 2019, 56(2):319-327.

HUANG J P, SHI Y H, GAO Y.Multi-scale Faster-RCNN algorithm for small object detection[J]. Journal of Computer Research and Development , 2019, 56(2):319-327. (in Chinese)

龙思源, 张葆, 宋策, 等.基于改进的加速鲁棒特征的目标识别[J].中国光学, 2017, 10(6):719-725.

LONG S Y, ZHANG B, SONG C, et al . Object detection based on improved speeded-up robust features[J]. Chinese Optics , 2017, 10(6):719-725. (in Chinese)

LIU L, OUYANG W L, WANG X G, et al . Deep learning for generic object detection: a survey[J]. arXiv preprint arXiv : 1809.02165, 2018. https://www.researchgate.net/publication/327550187_Deep_Learning_for_Generic_Object_Detection_A_Survey

GIRSHICK R, DONAHUE J, DARRELL T, et al . Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus, OH, USA: IEEE, 2014: 580-587.

HE K M, ZHANG X Y, REN S Q, et al . Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015, 37(9):1904-1916.

GIRSHICK R. Fast R-CNN[C]// Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago, Chile: IEEE, 2015: 1440-1448.

REN S Q, HE K M, GIRSHICK R, et al . Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017, 39(6):1137-1149.

HE K M, GKIOXARI G, DOLLAR P, et al . Mask R-CNN[C]// Proceedings of 2017 IEEE International Conference on Computer Vision . Venice, Italy: IEEE, 2017: 2980-2988.

REDMON J, DIVVALA S, GIRSHICK R, et al . You only look once: unified, real-time object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV, USA: IEEE, 2016: 779-788.

LIU W, ANGUELOV D, ERHAN D, et al . SSD: single shot multiBox detector[C]// Proceedings of 14 th European Conference on Computer Vision . Amsterdam, The Netherlands: Springer, 2016: 21-37.

LIN T Y, GOYAL P, GIRSHICK R, et al . Focal loss for dense object detection[C]// Proceedings of 2017 IEEE International Conference on Computer Vision . Venice, Italy: IEEE, 2017: 2999-3007.

FU C Y, LIU W, RANGA A, et al . DSSD: deconvolutional single shot detector[J]. arXiv preprint arXiv : 1701.06659, 2017. https://www.researchgate.net/publication/312759848_DSSD_Deconvolutional_Single_Shot_Detector

LIU S T, HUANG D, WANG Y H. Receptive field block net for accurate and fast object detection[C]// Proceedings of 15th European Conference on Computer Vision . Munich, Germany: Springer, 2018: 404-419.

ZHANG S F, WEN L Y, BIAN X, et al . Single-shot refinement neural network for object detection[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City, UT, USA: IEEE, 2018: 4203-4212.

梁华, 宋玉龙, 钱锋, 等.基于深度学习的航空对地小目标检测[J].液晶与显示, 2018, 33(9):793-800.

LIANG H, SONG Y L, QIAN F, et al . Detection of small target in aerial photography based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays , 2018, 33(9):793-800. (in Chinese)

ZHAO B, WU X, FENG J S, et al . Diversified visual attention networks for fine-grained object classification[J]. IEEE Transactions on Multimedia , 2017, 19(6):1245-1256.

HU H, GU J Y, ZHANG Z, et al . Relation networks for object detection[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City, UT, USA: IEEE, 2018: 3588-3597.

WANG F, JIANG M Q, QIAN C, et al . Residual attention network for image classification[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, HI, USA: IEEE, 2017: 6450-6458.

HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of 2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City, UT, USA: IEEE, 2018: 7132-7141.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]// Proceedings of the International Conference on Learning Representations . Banff, Canada, 2015.

SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Highway networks[J]. arXiv preprint arXiv : 1505.00387, 2015. https://www.researchgate.net/publication/275897262_Highway_Networks

EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al . The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision , 2010, 88(2):303-338.

HE K M, ZHANG X Y, REN S Q, et al . Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV, USA: IEEE, 2016: 770-778.

KONG T, YAO A B, CHEN Y R, et al . HyperNet: towards accurate region proposal generation and joint object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV, USA: IEEE, 2016: 845-853.

DAI J F, LI Y, HE K M, et al . R-FCN: object detection via region-based fully convolutional networks[C]// Proceedings of the 30 th International Conference on Neural Information Processing Systems . Barcelona, Spain: Curran Associates Inc., 2016: 379-387.

ZHU Y S, ZHAO C Y, WANG J Q, et al . CoupleNet: coupling global structure with local parts for object detection[C]// Proceedings of 2017 IEEE Internati onal Conference on Computer Vision . Venice, Italy: IEEE, 2017: 4146-4154.

REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, HI, USA: IEEE, 2017: 6517-6525.

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