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

作     者：Mingqiang GUO Hongting SHENG Zhizheng ZHANG Ying HUANG Xueye CHEN Cunjin WANG Jiaming ZHANG Mingqiang GUO;Hongting SHENG;Zhizheng ZHANG;Ying HUANG;Xueye CHEN;Cunjin WANG;Jiaming ZHANG

作者机构：School of Computer ScienceChina University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information SystemWuhan 430074China Wuhan Zondy Cyber Technology Co.Ltd.Wuhan 430074China Shenzhen Data Management Center of Planning and Natural ResourcesShenzhen 518000China Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources)Shenzhen 518000China College of EngineeringBoston UniversityBoston 02215USA 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2023年第17卷第6期

页      面：191-193页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

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) 

主　　题：hardware lighting weather 

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 ***,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy *** unfavorable conditions lead to a series of image degradations that seriously hamper machine vision *** 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.