This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden...This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.展开更多
Thailand has been on the World Health Organization(WHO)’s notorious deadliest road list for several years,currently ranking eighth on the list.Among all types of road fatalities,pickup trucks converted into vehicles ...Thailand has been on the World Health Organization(WHO)’s notorious deadliest road list for several years,currently ranking eighth on the list.Among all types of road fatalities,pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures,such as safety belts.Passenger overloading is illegal,but it is often overlooked.The country often uses police checkpoints to enforce traffic laws.However,there are few or no highway patrols to apprehend offending drivers.Therefore,in this study,we propose the use of existing closed-circuit television(CCTV)traffic cameras with deep learning techniques to classify overloaded public transport pickup trucks(PTPT)to help reduce accidents.As the said type of vehicle and its passenger occupancy characteristics are unique,a new model is deemed necessary.The contributions of this study are as follows:First,we used various state-of-the-art object detection YOLOv5(You Only Look Once)models to obtain the optimum overcrowded model pretrained on our manually labeled dataset.Second,we made our custom dataset available.Upon investigation,we compared all the latestYOLOv5 models and discovered that theYOLOv5L yielded the optimal performance with a mean average precision(mAP)of 95.1%and an inference time of 33 frames per second(FPS)on a graphic processing unit(GPU).We aim to deploy the selected model on traffic control computers to alert the police of such passenger-overloading violations.The use of a chosen algorithm is feasible and is expected to help reduce trafficrelated fatalities.展开更多
This paper first gives a SCP abstract model, then SCP’s overload detection and maximum processing capability are discussed quantitatively. Based upon dynamic adjustment, a new two-level SCP overload control algorithm...This paper first gives a SCP abstract model, then SCP’s overload detection and maximum processing capability are discussed quantitatively. Based upon dynamic adjustment, a new two-level SCP overload control algorithm is proposed. Theoretical analysis and simulation prove the algorithm’s effectiveness and fairness.展开更多
文摘This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.
基金This work was supported by(i)Suranaree University of Technology,(ii)Thailand Science Research and Innovation,and(iii)National Science Research and Innovation Fund(Grant Number:RU-7-706-59-03).
文摘Thailand has been on the World Health Organization(WHO)’s notorious deadliest road list for several years,currently ranking eighth on the list.Among all types of road fatalities,pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures,such as safety belts.Passenger overloading is illegal,but it is often overlooked.The country often uses police checkpoints to enforce traffic laws.However,there are few or no highway patrols to apprehend offending drivers.Therefore,in this study,we propose the use of existing closed-circuit television(CCTV)traffic cameras with deep learning techniques to classify overloaded public transport pickup trucks(PTPT)to help reduce accidents.As the said type of vehicle and its passenger occupancy characteristics are unique,a new model is deemed necessary.The contributions of this study are as follows:First,we used various state-of-the-art object detection YOLOv5(You Only Look Once)models to obtain the optimum overcrowded model pretrained on our manually labeled dataset.Second,we made our custom dataset available.Upon investigation,we compared all the latestYOLOv5 models and discovered that theYOLOv5L yielded the optimal performance with a mean average precision(mAP)of 95.1%and an inference time of 33 frames per second(FPS)on a graphic processing unit(GPU).We aim to deploy the selected model on traffic control computers to alert the police of such passenger-overloading violations.The use of a chosen algorithm is feasible and is expected to help reduce trafficrelated fatalities.
基金Supported by 863 high technology projectthe National Natural Science Foundation of ChinaKey Foundation of Ministry of Posts and Telecommunications
文摘This paper first gives a SCP abstract model, then SCP’s overload detection and maximum processing capability are discussed quantitatively. Based upon dynamic adjustment, a new two-level SCP overload control algorithm is proposed. Theoretical analysis and simulation prove the algorithm’s effectiveness and fairness.
