A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate...A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges.The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration.This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles.The motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also presented.The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles.The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments.The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility.It would be most difficult to do experiments on this large scale without the WARA-PS research arena.Furthermore,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.展开更多
为提高复杂气象条件下海上船舶的识别效果,通过暗通道先验去雾算法减少云雾遮挡对目标识别的影响,使用基于深度学习的YOLO(you only look once)改进算法提高目标识别效果。结果表明:采用的算法在中国航天科工四院指挥自动化中心的模拟...为提高复杂气象条件下海上船舶的识别效果,通过暗通道先验去雾算法减少云雾遮挡对目标识别的影响,使用基于深度学习的YOLO(you only look once)改进算法提高目标识别效果。结果表明:采用的算法在中国航天科工四院指挥自动化中心的模拟海事数据集上,4类船舶目标识别的m AP (mean average precision)达到89. 98%,超过了对比的其他目标识别算法;针对数据集中的云雾遮挡图像,暗通道去雾处理后,目标识别的m AP从53. 25%提升到69. 35%。可见提出的算法可以满足复杂气象条件下的海上船舶识别的需求。展开更多
基金All authors are partially supported by the Wallenberg AI,Autonomous Systems and Software Program(WASP)funded by the Knut and Alice Wallenberg Foundation.The first and second authors are additionally supported by the ELLIIT Network Organization for Information and Communication Technology,Swedenthe Swedish Foundation for Strategic Research SSF(Smart Systems Project RIT15-0097)+1 种基金The second author is also supported by a RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology,ChinaThe fifth and eighth authors are additionally supported by the Swedish Research Council.
文摘A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges.The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration.This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles.The motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also presented.The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles.The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments.The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility.It would be most difficult to do experiments on this large scale without the WARA-PS research arena.Furthermore,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.
文摘为提高复杂气象条件下海上船舶的识别效果,通过暗通道先验去雾算法减少云雾遮挡对目标识别的影响,使用基于深度学习的YOLO(you only look once)改进算法提高目标识别效果。结果表明:采用的算法在中国航天科工四院指挥自动化中心的模拟海事数据集上,4类船舶目标识别的m AP (mean average precision)达到89. 98%,超过了对比的其他目标识别算法;针对数据集中的云雾遮挡图像,暗通道去雾处理后,目标识别的m AP从53. 25%提升到69. 35%。可见提出的算法可以满足复杂气象条件下的海上船舶识别的需求。
