• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Zhang, Yi (Zhang, Yi.) | Zhuo, Li (Zhuo, Li.) | Ma, Chunjie (Ma, Chunjie.) | Zhang, Yutong (Zhang, Yutong.) | Li, Jiafeng (Li, Jiafeng.)

收录:

EI Scopus

摘要:

Fast and accurate prohibited object detection in X-ray images is great challenging. Based on YOLOv6 object detection framework, in this paper, Channel-Target Attention Feature Pyramid Network (CTA-FPN) is proposed for prohibited object detection in X-ray images. It includes two key components: TAAM (Target Aware Attention Module) and CAM (Channel Attention Module). TAAM is to generate the target attention map to enhance the features of prohibited object regions and suppress those of the background regions, so as to solve the problems of object occlusion and cluttered background in X-ray images. CAM is to highlight the feature channels important to the detection tasks, and suppress the irrelevant ones. The target-wise and channel-wise feature enhancement can effectively strengthen the feature representation capability of the network. The proposed CTA-FPN is incorporated into S, M and L models of YOLOv6 respectively, obtaining three X-ray prohibited object detection models. The experimental results on two publicly available benchmark datasets of SIXray and CLCXray show that, CTA-FPN can effectively improve the detection performance of YOLOv6. Especially, YOLOv6-CTA-FPN-L can achieve the state-of-the-arts detection accuracy. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词:

Object detection Object recognition Feature extraction Benchmarking Cams Image enhancement

作者机构:

  • [ 1 ] [Zhang, Yi]Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Faculty of Information, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Zhuo, Li]Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Faculty of Information, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Ma, Chunjie]Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Faculty of Information, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Zhang, Yutong]Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Faculty of Information, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Li, Jiafeng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Faculty of Information, Beijing University of Technology, Beijing; 100124, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

来源 :

Sensing and Imaging

ISSN: 1557-2064

年份: 2023

期: 1

卷: 24

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:573/4980930
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司