• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (11): 1995-2002.

• 图形与图像 • 上一篇    下一篇

基于无锚框目标检测算法的多样性感受野注意力特征补偿

张海燕1,付应娜1,丁桂江2,孟庆岩2   

  1. (1.合肥工业大学计算机与信息学院,安徽 合肥 231009; 2.三维医疗科技股份有限公司,江苏 徐州 221000)

  • 收稿日期:2021-01-25 修回日期:2021-06-23 接受日期:2022-11-25 出版日期:2022-11-25 发布日期:2022-11-25
  • 基金资助:
    安徽省重点研究与开发计划(201904d07020018)

Towards Anchor-free object detection with diverse receptive fields attention feature refinement network

ZHANG Hai-yan1,FU Ying-na1,DING Gui-jiang2,MENG Qing-yan2   

  1. (1.School of Computer and Information,Hefei University of Technology,Hefei 231009;2.3D Medical Technology Co.,Ltd.,Xuzhou 221000,China)
  • Received:2021-01-25 Revised:2021-06-23 Accepted:2022-11-25 Online:2022-11-25 Published:2022-11-25

摘要: 作为目标检测的研究热点之一,无锚框算法摒弃大量预定义框的设置而采取逐像素的方式进行预测。即便如此,它仍不能够很好地处理重叠目标。此外,该算法获取图像的全局信息能力较弱且易出现感受野不匹配。因此,提出2种改进方法:多样性感受野注意力机制和全局信息指导特征融合。PASCAL VOC和MS COCO数据集上广泛的实验证实了改进方法的有效性。与基线FCOS相比,本文方法的检测精度在PASCAL VOC上提升了1.4%,在MS COCO上的精确度为42.8%,检测性能明显优于许多先进算法。

关键词: 无锚框, 多样性感受野, 注意力机制, 特征融合, 目标检测

Abstract: As one of the research hotspots of object detection, anchor free abandons a large number of predefined box Settings and adopts pixel-by-pixel method for prediction. Even so, it does not deal well with overlapping objects. In addition, the ability of network to obtain global information of images is weak and receptive field mismatch is easy to occur. Therefore, this paper proposes two modules: diverse receptive field attention mechanism (DRAM) and global context-guided feature fusion module (GCF). Extensive experiments on the PASCAL VOC and MS COCO confirm the effectiveness of our method. Compared with the baseline FCOS, the proposed method can improve PASCAL VOC by 1.4 points and obtain a mAP of 42.8 on MS COCO. The detection performance is significantly better than many advanced algorithms.  

Key words: anchor-free, diverse receptive field, attention mechanism, feature fusion, object detection