딥 러닝 이미지 분류 기법을 이용한 해충 방제 시스템
Pest Control System using Deep Learning Image Classification Method
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this paper, we propose a layer structure of a pest image classifier model using CNN (Convolutional Neural Network) and background removal image processing algorithm for improving classification accuracy in order to build a smart monitoring system for pine wilt pest control. In this study, we have constructed and trained a CNN classifier model by collecting image data of pine wilt pest mediators, and experimented to verify the classification accuracy of the model and the effect of the proposed classification algorithm. Experimental results showed that the proposed method successfully detected and preprocessed the region of the object accurately for all the test images, resulting in showing classification accuracy of about 98.91%. This study shows that the layer structure of the proposed CNN classifier model classified the targeted pest image effectively in various environments. In the field test using the Smart Trap for capturing the pine wilt pest mediators, the proposed classification algorithm is effective in the real environment, showing a classification accuracy of 88.25%, which is improved by about 8.12% according to whether the image cropping preprocessing is performed. Ultimately, we will proceed with procedures to apply the techniques and verify the functionality to field tests on various sites.
Abstract
In this paper, we propose a layer structure of a pest image classifier model using CNN (Convolutional Neural Network) and background removal image processing algorithm for improving classification accuracy in order to build a smart monitoring system for pine wilt pest control. In this study, we have constructed and trained a CNN classifier model by collecting image data of pine wilt pest mediators, and experimented to verify the classification accuracy of the model and the effect of the proposed classification algorithm. Experimental results showed that the proposed method successfully detected and preprocessed the region of the object accurately for all the test images, resulting in showing classification accuracy of about 98.91%. This study shows that the layer structure of the proposed CNN classifier model classified the targeted pest image effectively in various environments. In the field test using the Smart Trap for capturing the pine wilt pest mediators, the proposed classification algorithm is effective in the real environment, showing a classification accuracy of 88.25%, which is improved by about 8.12% according to whether the image cropping preprocessing is performed. Ultimately, we will proceed with procedures to apply the techniques and verify the functionality to field tests on various sites.
참고문헌 (21)
- "Pine Wilt Disease"[URL]
- "Pheromone tempted trap for Pine Wilt Desease mediators"[URL]
- "Pine Wilt Insect...‘Captured using pheromone tempted trap"[URL]
- N. Dalal, "Histograms of oriented gradients for human detection", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)
- "Scikits-Image, "Histogram of Oriented Gradients""[URL]
- David G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, 60(2), pp.91~110 (2004)
- Corinna Cortes, "Support-Vector Networks", Machine Learning, 20(3), pp.273~297 (1995)
- J. R. Quinlan, "Induction of Decision Trees", Machine Learning, 1(1), pp.81~106 (1986)
- Dmitry Yu., "Decision Stream : Cultivating Deep Decision Trees", IEEE-ICTAI, pp.905~912 (2017)
- Leo Breiman, "Random Forests", Machine Learning, 45(1), pp.5~32 (2001)
- Bagging predictors, "Leo Breiman", Department of Statistics, University of California at Berkeley, ~ (1994)
- "Random Forest Simple Explan ation" (2017)[URL]
- Limiao Deng, "Pest Recognition System Based on Bio-Inspired Filtering and LCP Features", Internatinal Computer Conference on Wavelet Active Media Technology and Information Processing, pp.202~204 (2015)
- Ronny Hänsch, "Feature independent classification of hyperspectral images by projection-based random forests", Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.1~4 (2015)
- Zhiqiang Qiao, "Application of SVM based on genetic algorithm in classification of cataract fundus images", IEEE International Conference on Imaging Systems and Techniques, pp.1~5 (2017)
- Tsung-Han Chan, "PCANet : A Simple Deep Learning Baseline for Image Classification?", IEEE Transactions on Images Processing, 24(12), pp.5017~5032 (2015)
- 최선욱, "하이퍼그래프 모델 기반의 장면 이미지 분류 기법", 한국지능시스템학회 논문지, 24(2), pp.166~172 (2014), 한국지능시스템학회
- "Raspberry Pi 3"[URL]
- Jean Serra, "Image Analysis and Mathematical Morphology, Volume 2 : Theoretical Advances", Journal of Microscopy, 152(2), pp.597~597 (1988)
- J. R. R. Uijlings, "Selective Search for Object Recognition", International Journal of Computer Vision, 104(2), pp.154~171 (2013)
- Pedro F. Felzenszwalb, "Object Detection with Discriminatively Trained Part-Based Models", IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), pp.1627~1645 (2010)