X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out wo...X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.展开更多
Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image t...Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.展开更多
文摘X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.
基金supported by the Healthcare AI Convergence R&D Program through the National IT Industry Promotion Agency of Korea(NIPA)funded by the Ministry of Science and ICT(No.S0102-23-1007)the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2017R1A6A1A03015496).
文摘Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.