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http://dx.doi.org/10.5762/KAIS.2017.18.12.157

Object Segmentation for Detection of Moths in the Pheromone Trap Images  

Kim, Tae-Woo (Division of Electrical, Electronic and Communication Engineering, Hanyang Cyber University)
Cho, Tae-Kyung (Dept. of Information Security Engineering, Sangmyung University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.18, no.12, 2017 , pp. 157-163 More about this Journal
Abstract
The object segmentation approach has the merit of reducing the processing cost required to detect moths of interest, because it applies a moth detection algorithm to the segmented objects after segmenting the objects individually in the moth image. In this paper, an object segmentation method for moth detection in pheromone trap images is proposed. Our method consists of preprocessing, thresholding, morphological filtering, and object labeling processes. Thresholding in the process is a critical step significantly influencing the performance of object segmentation. The proposed method can threshold very elaborately by reflecting the local properties of the moth images. We performed thresholding using global and local versions of Ostu's method and, used the proposed method for the moth images of Carposina sasakii acquired on a pheromone trap placed in an orchard. It was demonstrated that the proposed method could reflect the properties of light and background on the moth images. Also, we performed object segmentation and moth classification for Carposina sasakii images, where the latter process used an SVM classifier with training and classification steps. In the experiments, the proposed method performed the detection of Carposina sasakii for 10 moth images and achieved an average detection rate of 95% of them. Therefore, it was shown that the proposed technique is an effective monitoring method of Carposina sasakii in an orchard.
Keywords
Carposina sasakii; Moth detection; Moth image; Object segmentation; Pheromone trap; SVM classifier;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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