摘要
随着经济高速发展,经略海洋与海洋经济发展成为国家重要发展战略,港口设施建设和完善对于海洋经济发展有着重要作用。然而,港口重型机械以及轮机操作对作业人员安全有潜在威胁,智能化、信息化码头建设势在必行。近年计算机视觉和深度学习技术快速发展,为港口应用智能视觉技术提供了有力的技术支撑。基于深度学习框架YOLOV4搭建了港口作业人员目标检测平台,在自建港口收集并整理了一个大规模作业人员视频数据集,在该数据集上实现不同作业场景下港口作业人员的精确检测。在自建港口作业人员数据集上将FasterRCNN、SSD和YOLOV4三种目标检测框架进行实验对比,结果表明,YOLOV4的平均检测准确率优于其它目标检测框架。基于YOLOV4的港口作业人员检测系统应用提高了港口信息化建设进度,提高了港口作业人员的安全性。
The development of strategic marine and marine economy has become an important strategy for national development with the rapid development of economy.The construction and improvement of harbor facilities play an important role in the development of marine economy.However,the operation of heavy machinery and marine machinery in the harbor scenes poses a potentialsafety risks to the operators,hence,the construction of intelligent and information-based harbors is imperative.In recent years,the rapid development of computer vision and deep learning technology has provided strong technical support for the application of intelligent vision technology in harbor scenes.In this paper,we build an operator detection platform based on the deep learning framework YOLOV4.We collect and sort out a large-scale harbor operator data set in a self-built harbor,and realize accurate detection of harbor operators in different operating scenarios on this data set.In our experiments,we compare three state-of-the art object detection frameworks,i.e.,Faster RCNN,SSD and YOLOV4.Extensive experiments on the self-built harbor operator data set shows that the mean Accuracy Precision of YOLOV4 is better than other object detection frameworks.The YOLOV4-based harbor operator detection technology has improved the progress of informatization construction to a certain extent and improved the safety of harbor operators.
作者
程国安
王浩
王胜科
CHENG Guo-an;WANG Hao;WANG Sheng-ke(School of Information and Electrical Engineering,Qingdao Harbour Vocational&Technical College,Qingdao 266404,China;School of Computer Science and Technology,Ocean University of China,Qingdao 266100,China)
出处
《软件导刊》
2022年第3期95-99,共5页
Software Guide
基金
国家自然科学基金项目(41927805,U17062189,61602229,41606198,61501417,41706010)
国家重点研发计划专项(2018YFB1701802)
装备预研教育部联合基金青年项目(6141A020337)
山东省自然科学基金项目(ZR2018ZB0852,ZR2016FM13,ZR2016FB02)
青岛港湾职业技术学院科研项目(QDGW2018Z09)。
