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http://dx.doi.org/10.7471/ikeee.2020.24.4.1141

Real Time Hornet Classification System Based on Deep Learning  

Jeong, Yunju (SW Convergence Education Center, Andong National University)
Lee, Yeung-Hak (SW Convergence Education Center, Andong National University)
Ansari, Israfil (Smart Vision Tech.)
Lee, Cheol-Hee (Dept. of Computer Engineering, Andong National University)
Publication Information
Journal of IKEEE / v.24, no.4, 2020 , pp. 1141-1147 More about this Journal
Abstract
The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.
Keywords
Hornet classification; Deep learning; Object Detection; Object labeling; Mish function; Spatial attention module;
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