Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
Based on experiment data of the Sino-German comprehensive investigations in the Bohai Sea in 1998 and 1999, a simple coupled pelagic-benthic ecosystem multi-box model is used to simulate the ecosystem seasonal variati...Based on experiment data of the Sino-German comprehensive investigations in the Bohai Sea in 1998 and 1999, a simple coupled pelagic-benthic ecosystem multi-box model is used to simulate the ecosystem seasonal variation. The pelagic sub-model consists of seven state variables： phytoplankton, zooplankton, TIN, TIP, DOC, POC and dissolved oxygen （DO）. The benthic sub-model includes macro-benthos, meiobenthos, bacteria, detritus, TIN and TIP in the sediment. Besides the effects of solar radiation, water temperature and the nutrient from sea bottom exudation, land-based inputs are considered. The impact of the advection terms between the boxes is also considered. Meanwhile, the effects of the micro- bial-loop are introduced with a simple parameterization. The seasonal variations and the horizontal distributions of the ecosystem state variables of the Bohai Sea are simulated. Compared with the observations, the results of the multi-box model are reasonable. The modeled results show that about 13% of the photosynthesis primary production goes to the main food loop, 20% transfers to the benthic domain, 44% is consumed by the respiration of phytoplankton, and the rest goes to DOC. Model results also show the importance of the microbial food loop in the ecosystem of the Bohai Sea, and its contribution to the annual zooplankton production can be 60%-64%.展开更多
Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is co...Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their identity.Hence,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the CriminalDatabase.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.展开更多
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金supported by the National Natural Science Foundation of China(Nos.G49790010 and 40476045).
文摘Based on experiment data of the Sino-German comprehensive investigations in the Bohai Sea in 1998 and 1999, a simple coupled pelagic-benthic ecosystem multi-box model is used to simulate the ecosystem seasonal variation. The pelagic sub-model consists of seven state variables： phytoplankton, zooplankton, TIN, TIP, DOC, POC and dissolved oxygen （DO）. The benthic sub-model includes macro-benthos, meiobenthos, bacteria, detritus, TIN and TIP in the sediment. Besides the effects of solar radiation, water temperature and the nutrient from sea bottom exudation, land-based inputs are considered. The impact of the advection terms between the boxes is also considered. Meanwhile, the effects of the micro- bial-loop are introduced with a simple parameterization. The seasonal variations and the horizontal distributions of the ecosystem state variables of the Bohai Sea are simulated. Compared with the observations, the results of the multi-box model are reasonable. The modeled results show that about 13% of the photosynthesis primary production goes to the main food loop, 20% transfers to the benthic domain, 44% is consumed by the respiration of phytoplankton, and the rest goes to DOC. Model results also show the importance of the microbial food loop in the ecosystem of the Bohai Sea, and its contribution to the annual zooplankton production can be 60%-64%.
文摘Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their identity.Hence,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the CriminalDatabase.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.
