Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for...Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.展开更多
Technological advances in computer science and their application in our daily life allow us to improve our understanding of problems and solve them effectively.A system design to detect people with fever and determine...Technological advances in computer science and their application in our daily life allow us to improve our understanding of problems and solve them effectively.A system design to detect people with fever and determine highrisk areas using infrared thermography and big data is presented.In order to detect people with fever,face detection algorithms of Viola-Jones and Kanade-Lucas are investigated,and comparison between them is presented using a training set of 406 thermal images and a test set of 2072 thermal images.Thermography analysis is performed on detected faces to obtain the temperature level on Celsius scale.With this information a sample database is created.To perform big data experimental analysis,Power Bi tool is used to determine the high-risk area.The experimental results show that Viola-Jones algorithm has a higher performance recognizing faces of thermal images than KanadeLucas,having a high detection rate,less false-positives rate and false-negatives rate.展开更多
基金supported in part by the National Key Research and Development Program of China(No. 2018YFC0309104)the Construction System Science and Technology Project of Jiangsu Province (No.2021JH03)。
文摘Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.
文摘Technological advances in computer science and their application in our daily life allow us to improve our understanding of problems and solve them effectively.A system design to detect people with fever and determine highrisk areas using infrared thermography and big data is presented.In order to detect people with fever,face detection algorithms of Viola-Jones and Kanade-Lucas are investigated,and comparison between them is presented using a training set of 406 thermal images and a test set of 2072 thermal images.Thermography analysis is performed on detected faces to obtain the temperature level on Celsius scale.With this information a sample database is created.To perform big data experimental analysis,Power Bi tool is used to determine the high-risk area.The experimental results show that Viola-Jones algorithm has a higher performance recognizing faces of thermal images than KanadeLucas,having a high detection rate,less false-positives rate and false-negatives rate.
