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http://dx.doi.org/10.22156/CS4SMB.2021.11.09.028

Lightweight Convolution Module based Detection Model for Small Embedded Devices  

Park, Chan-Soo (Dept. of Plasma Bio Display, KwangWoon University)
Lee, Sang-Hun (Ingenium College of Liberal Arts, KwangWoon University)
Han, Hyun-Ho (College of General Education, University of Ulsan)
Publication Information
Journal of Convergence for Information Technology / v.11, no.9, 2021 , pp. 28-34 More about this Journal
Abstract
In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.
Keywords
Deep Learning; Object Detection; Lightweight; Raspberry Pi; Embedded;
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