Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent...Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO detection.The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on sUAS.This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)sensor.Specifically,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.展开更多
In order to explore the unexploded ordnance problem of cluster munitions and find the so- lutions, an M85 sub-munitions reliability model was established by applying the Monte Carlo method. Simulation and experimental...In order to explore the unexploded ordnance problem of cluster munitions and find the so- lutions, an M85 sub-munitions reliability model was established by applying the Monte Carlo method. Simulation and experimental statistics matched the proportion of unexploded ordnance, so the hy- pothesis was feasible. The causes of failure and influencing factors of the dual-purpose improved conventional munitions M85 were analyzed according to experimental data. The sensitivity of each device in fuze was also analyzed. The sorting of weight of each device influence in M85 sub-muni- tions fuze was determined. Stabilization device with the maximum weight is the key components of sub-munitions fuze, so these results provide a reference to the analysis and redesign of other sub- munitions fuzes.展开更多
This paper aims to provide the reader with the results of the Unexploded Ordnance(UXO)survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast,duri...This paper aims to provide the reader with the results of the Unexploded Ordnance(UXO)survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast,during the Second World War.This UXO survey was carried out between 2015-2018 by the Romanian Navy’s hydrographic ship“Commander Alexandru Cătuneanu”and Romanian Mine Warfare Data Center,using towed side-scan sonar technology and oceanographic observations.After explaining the materials and methodology,the results are presented and discussed:mosaics of the minefields,side-scan images of UXO contacts,side-scan images of the wrecks that were sunk in the minefields and some visible natural geological features of the seafloor.It was concluded that most of the objects discovered are sinkers,wreck debris or parts of chains,which does not represent a danger to navigation.展开更多
基金the Office of Naval Research for supporting this effort through the Consortium for Robotics and Unmanned Systems Education and Research。
文摘Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO detection.The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on sUAS.This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)sensor.Specifically,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.
基金Supported by Defence Science and Technology Laboratory( 3020012251002)
文摘In order to explore the unexploded ordnance problem of cluster munitions and find the so- lutions, an M85 sub-munitions reliability model was established by applying the Monte Carlo method. Simulation and experimental statistics matched the proportion of unexploded ordnance, so the hy- pothesis was feasible. The causes of failure and influencing factors of the dual-purpose improved conventional munitions M85 were analyzed according to experimental data. The sensitivity of each device in fuze was also analyzed. The sorting of weight of each device influence in M85 sub-muni- tions fuze was determined. Stabilization device with the maximum weight is the key components of sub-munitions fuze, so these results provide a reference to the analysis and redesign of other sub- munitions fuzes.
文摘This paper aims to provide the reader with the results of the Unexploded Ordnance(UXO)survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast,during the Second World War.This UXO survey was carried out between 2015-2018 by the Romanian Navy’s hydrographic ship“Commander Alexandru Cătuneanu”and Romanian Mine Warfare Data Center,using towed side-scan sonar technology and oceanographic observations.After explaining the materials and methodology,the results are presented and discussed:mosaics of the minefields,side-scan images of UXO contacts,side-scan images of the wrecks that were sunk in the minefields and some visible natural geological features of the seafloor.It was concluded that most of the objects discovered are sinkers,wreck debris or parts of chains,which does not represent a danger to navigation.
