Study on effect of source to film distance (SFD) on the radiographic images

Arifin, Nurul Laili; Widiastuti, Hanifah; Wibowo, Ari

doi:10.1109/incae.2018.8579376

Cited by 2 publications

(2 citation statements)

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“…e representative algorithms of the latter are S3FD [22] and SSH [23]. Compared with traditional learning methods, detection methods based on deep learning do not require manual feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

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Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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“…The former's representative algorithms are Faster-Rcnn [36] and MTCNN [37]. The latter's representative algorithms are S3FD [38], SSH [39]. Compared with traditional methods [40], face detection based on convolutional neural network(CNN) avoids the artificial extraction of features.…”

Section: A Face Detection Based On the Improved Yolov3-tiny Networkmentioning

confidence: 99%

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

2020

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Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. It results in some inaccuracy. The paper proposes a fatigue driving detection algorithm based on facial multifeature fusion combining driver characteristics. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, on the basis of the Dlib toolkit, we introduce the Eye Feature Vector(EFV) and Mouth Feature Vector(MFV), which are the evaluation parameters of the driver's eye state and mouth state, respectively. Then, the driver identity information library is constructed by offline training, including driver eye state classifier library, driver mouth state classifier library, and driver biometric library. Finally, we construct the driver identity verification model and the driver fatigue assessment model by online assessment. After passing the identity verification, calculate the driver's closed eyes time, blink frequency and yawn frequency to evaluate the driver's fatigue state. In simulated driving applications, our algorithm detects the fatigue state at a speed of over 20fps with an accuracy of 95.10%.

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Study on effect of source to film distance (SFD) on the radiographic images

Cited by 2 publications

References 0 publications

Driver Fatigue Detection Based on Facial Key Points and LSTM

Driver Fatigue Detection Based on Facial Key Points and LSTM

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

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