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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Nachirat Rachburee
Rajamangala University of Technology Thanyaburi
Computer Science & IT Electrical & Electronics Engineering Engineering

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An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people Nachirat Rachburee; Wattana Punlumjeak
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3434-3442

Abstract

The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.
Lotus species classification using transfer learning based on VGG16, ResNet152V2, and MobileNetV2 Nachirat Rachburee; Wattana Punlumjeak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Technology has played an increasingly important role in daily life. Especially, technology in object classification that comes in to make human life more comfortable as well as to help people of all ages learning unlimited in anywhere, anytime. Lotus Museum located in Rajamangala University of Technology Thanyaburi (RMUTT) that is open to the general public to learn as well as to cultivate awareness for propagation and result in future preservation. In this paper, we proposed lotus species classification with three pre-trained weights in the ImageNet dataset: visual geometry group (VGG16), residual neural network (ResNet152V2), and MobileNetV2. Fine-turning is used in the last layer after retrained with the custom data we provided. The experimental result shows the accuracy of VGG16, ResNet152V2, and MobileNetV2 are 98.5%, 98.0%, and 99.5% respectively. Therefore, MobileNetV2 not only gives the best accuracy than others but also uses the lowest parameters which are effective in computation time and proper to mobile devices. The proposed research paper on lotus classification base on transfer learning is an effective way to encourage and support people to learn without limitations.
Co-Authors Wattana Punlumjeak