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http://dx.doi.org/10.6109/jkiice.2022.26.6.813

Efficient Object Recognition by Masking Semantic Pixel Difference Region of Vision Snapshot for Lightweight Embedded Systems  

Yun, Heuijee (School of Electronic Engineering, Kyungpook National University)
Park, Daejin (School of Electronic Engineering, Kyungpook National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.6, 2022 , pp. 813-826 More about this Journal
Abstract
AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.
Keywords
Object detection; OpenCV; ENet; YOLO; Deep learning;
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Times Cited By KSCI : 5  (Citation Analysis)
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