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http://dx.doi.org/10.6109/jkiice.2020.24.12.1581

Object Detection Model Using Attention Mechanism  

Kim, Geun-Sik (Department of Information Convergence Engineering, Pusan National University)
Bae, Jung-Soo (College of Software Convergence, Dongseo University)
Cha, Eui-Young (Department of Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.12, 2020 , pp. 1581-1587 More about this Journal
Abstract
With the emergence of convolutional neural network in the field of machine learning, the model for solving image processing problems has seen rapid development. However, the computing resources required are also rising, making it difficult to learn from a typical environment. Attention mechanism is originally proposed to prevent the gradient vanishing problem of the recurrent neural network, but this can also be used in a direction favorable to learning of the convolutional neural network. In this paper, attention mechanism is applied to convolutional neural network, and the excellence of the proposed method is demonstrated through the comparison of learning time and performance difference at this time. The proposed model showed that both learning time and performance were superior in object detection based on YOLO compared to models without attention mechanism, and experimentally demonstrated that learning time could be significantly reduced. In addition, this is expected to increase accessibility to machine learning by end users.
Keywords
Machine learning; Object detection; Attention mechanism; CNN(Convolutional neural network);
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