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2 Annals of Computer Science and Information Systems

2 2022

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Safety-Assisted Driving Technology Based on Artificial Intelligence and Machine Learning for Moving Vehicles in Vietnam

Vu Hong-Son, Nguyen Van-Hien

Annals of Computer Science and Information Systems

2022

Vol. 33

279--284

ADAS (Advanced Driver Assistance Systems) plays an important role in building a safe and modern traffic system. For these systems, precise detection performance and response speed are critical. However, the detection of mobile vehicles is facing many difficulties due to the density of vehicles, the complex background scene in the city, etc. In addition, the detection and identification requirements respond in real time is also a challenge for current systems. This paper proposes a model using deep learning algorithms and artificial intelligence to increase accuracy and improve response speed for intelligent driving assistance systems. Accordingly, this paper proposes the YOLO (You Only Look One) model together with a sample data set collected and classified separately suitable for Vietnam traffic and our training algorithm. The experimental results were then performed on an NVIDIA Jetson TX2 embedded computer. The experimental results show that, the proposed method has increased the speed by at least 1.5 times with the detection rate reaching 79\% for the static camera system; and speed up at least 1.5x with a detection rate of 89\% for the dynamic camera system at 1280x720px high resolution images.

A Flexible Approach for Automatic Door Lock Using Face Recognition

Vu Hong-Son, Ly Van-Dat

Annals of Computer Science and Information Systems

2022

Vol. 33

157--163

The model of smart door lock using face recognition based on hardware is the Jetson TX2 embedded computer proposed in this paper. In order to recognize the faces, face detection is a very important step. This paper studies and evaluates two methods of face detection, namely Histograms of Oriented Gradients (HOG) method which represents the approach using facial features and Multi-task Cascaded Convolutional Neural Networks method (MTCNN) represents using of deep learning and neural networks. To evaluate these two methods, the experimental model is used to verify the hardware platform, which is the Jetson TX2 embedded computer. The face angle parameter is used to rate the detection level and accuracy for each method. In addition, the experimental model also evaluates the speed of face detection from the camera of these methods. Experimental results show that the average time for face detection by HOG and MTCNN method are respectively 0.16s and 0.58s. For face-to-face frames, both methods detect very well with an accuracy rate of 100\%. However, with various face angles of 30o, 60o, 90o, the MTCNN method gives more accurate results, which is also consistent with published studies. The smart door lock model uses the MTCNN face detection method combined with the Facenet algorithm along with a data set of 200 images for 1 face with accuracy of 99\%.