Improved YOLOv4 network using infrared images for personnel detection in coal mines
Author:
Affiliation:
1. China University of Mining and Technology (Beijing), School of Mechanical Electronics and Informatio
2. China Energy Ningxia Coal Co., Ltd. Shuangma Coal Mine, Yinchuan
Funder
National Natural Science Foundation of China
Publisher
SPIE-Intl Soc Optical Eng
Subject
Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics
Cited by 21 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A real-time detection for miner behavior via DYS-YOLOv8n model;Journal of Real-Time Image Processing;2024-05-13
2. CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background;Journal of Electronic Imaging;2024-04-16
3. Borehole Depth Recognition Based on Improved YOLOX Detection;The Computer Journal;2024-02-11
4. Rep-YOLO: an efficient detection method for mine personnel;Journal of Real-Time Image Processing;2024-02-06
5. Detection of Underground Dangerous Area Based on Improving YOLOV8;Electronics;2024-02-02
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