딥러닝과 확률모델을 이용한 실시간 토마토 개체 추적 알고리즘
Real-Time Tomato Instance Tracking Algorithm by using Deep Learning and Probability Model
0
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.
Abstract
Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.
참고문헌 (17)
- O. O. Arjenaki, "Online tomato sorting based on shape, maturity, size, and surface defects using machine vision", Turkish J. Agric. For, 37, pp.62~68 (2013)[DOI]
- Megha.P. Arakeri, "Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry", Procedia Computer Science, 79, pp.426~433 (2016), Elsevier BV[DOI]
- C. Li, "A method for color classification of fruits based on machine vision", WSEAS Trans. Syst, 8(2) (2009)[DOI]
- Nashwa El-Bendary, "Using machine learning techniques for evaluating tomato ripeness", Expert Systems with Applications, 42(4), pp.1892~1905 (2015), Elsevier BV[DOI]
- Yang-Yang Zheng, "CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture", Sensors, 19(5), pp.1058~ (2019), MDPI AG[DOI]
- 고광은, "객체 탐색용 딥러닝 모델 기반 실시간 토마토 숙도 분류 시스템", 제어.로봇.시스템학회 논문지, 24(11), pp.999~1004 (2018), 제어·로봇·시스템학회[DOI]
- Nidhi Goel, "Fuzzy classification of pre-harvest tomatoes for ripeness estimation – An approach based on automatic rule learning using decision tree", Applied Soft Computing, 36, pp.45~56 (2015), Elsevier BV[DOI]
- Guoxu Liu, "A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis", Sensors, 19(9), pp.2023~ (2019), MDPI AG[DOI]
- Arti Singh, "Machine Learning for High-Throughput Stress Phenotyping in Plants", Trends in Plant Science, 21(2), pp.110~124 (2016), Elsevier BV[DOI]
- Guillermo L. Grinblat, "Deep learning for plant identification using vein morphological patterns", Computers and Electronics in Agriculture, 127, pp.418~424 (2016), Elsevier BV[DOI]
- "YOLOv3: An Incremental Improvement" (2018)[URL]
- Shaoqing Ren, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), pp.1137~1149 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- J. Redmon, "YOLO9000: better, faster, stronger", 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.7263~7271 (2017)
- K. He, "Deep residual learning for image recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770~778 (2016)
- "The Discrete Kalman Filter" (1995)[URL]
- Sebastian Thrun, "Probabilistic robotics", Communications of the ACM, 45(3), pp.52~57 (2002), Association for Computing Machinery (ACM)[DOI]
- "Darknet: Open source neural networks inc" (2013)[URL]