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

查看全文 作　　者：Mingqiang [1,2]GUO;Hongting [1,2]SHENG;Zhizheng [1,2]ZHANG;Ying [3]HUANG;Xueye [4,5]CHEN;Cunjin [4,5]WANG;Jiaming [6]ZHANG

机构地区：[1]School of Computer Science,China University of Geosciences,Wuhan 430074,China;[2]National Engineering Research Center of Geographic Information System,Wuhan 430074,China;[3]Wuhan Zondy Cyber Technology Co.Ltd.,Wuhan 430074,China;[4]Shenzhen Data Management Center of Planning and Natural Resources,Shenzhen 518000,China;[5]Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources),Shenzhen 518000,China;[6]College of Engineering,Boston University,Boston 02215,USA

出　　处：《Frontiers of Computer Science》索引2023年第17卷第6期,共3页

基　　金：funded by the National Natural Science Foundation of China(Grant Nos.41971356,41701446);the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(KF-2022-07-001). 摘　　要：1 Introduction With the rapid progress of Artificial Intelligence(AI)technology in object detection and face recognition,deep learning methods for face mask wearing detection have become increasingly mature and continuously take into account the needs of efficiency and accuracy.However,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy weather.These unfavorable conditions lead to a series of image degradations that seriously hamper machine vision tasks.Although camera parameter adjustment,auxiliary lighting,or pre-processing enhancement[1]can weaken these negative effects to some extent to promote the detection,they will also result in increased hardware and time costs. 关键词：HARDWARE LIGHTING WEATHER

相关文献

参考文献（5）
引证文献（0）
耦合文献（1）

参考文献(5)

耦合文献(1)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

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

参考文献(5)

耦合文献(1)

用户提示