CW-YOLO: joint learning for mask wearing detection in low-light conditions

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers of Computer Science Pub Date : 2023-12-02 DOI:10.1007/s11704-023-3351-y

Mingqiang Guo, Hongting Sheng, Zhizheng Zhang, Ying Huang, Xueye Chen, Cunjin Wang, Jiaming Zhang

引用次数: 0

Abstract

Comprehensive comparison results on the above two datasets indicate that the detection improvements proposed in CWYOLO framework for low-light conditions are effective and can stand out among the existing excellent method. In future work, we would explore a more efficient and lightweight network architecture with group convolution to advance the mobile deployment of the detection framework.

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CW-YOLO:微光条件下口罩佩戴检测联合学习

在上述两个数据集上的综合对比结果表明，CWYOLO框架在弱光条件下的检测改进是有效的，可以在现有的优秀方法中脱颖而出。在未来的工作中，我们将探索一种更高效、更轻量级的网络架构，利用群卷积来推进检测框架的移动部署。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.