AI 머신비전을 이용한 어류의 목표중량 절단지점 예측기법
A technique for predicting the cutting points of fish for the target weight using AI machine vision
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초록
본 논문에서는 이러한 어류 가공 현장의 문제점을 개선하기 위해서 AI 머신 비전을 이용한 어류의 목표 중량 절단 예측기법을 제안한다. 제안하는 방법은 먼저 입력된 물고기의 평면도와 정면도를 촬영하여 이미지기반의 전처리를 수행한다. 그런 다음 RANSAC(RANdom SAMmple Consensus)를 사용하여 어류의 윤곽선을 추출한 다음 3D 모델링을 사용하여 물고기의 3D 외부 정보를 추출한다. 이어서 추출된 3차원 특징 정보와 측정된 중량 정보를 머신러닝하여 목표 중량에대한 절단 지점을 예측하기 위한 신경망 모델을 생성한다. 마지막으로 제안기법을 통해 예측된절단 지점으로 직접 절단한 뒤 그 중량을 측정하였다. 그리고 측정된 무게를 목표 무게와 비교하여 MAE(Mean Absolute Error) 와 MRE(Mean Relative Error)와 같은 평가 방법을 사용해 성능을 평가하였다. 그 결과, 목표 중량과 비교해 3% 이내의 평균 오차율을 달성하였다. 제안된 기법은 향후 자동화 시스템과 연계되어 수산업 발전에 크게 기여할 것으로 전망한다.
Abstract
In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.
참고문헌 (36)
- G. Merino, "Can marine fisheries and aquaculture meet fish demand from a growing human population in a changing climate", Global Environmental Change, 22(4), pp.795~806 (2012)[DOI]
- N. N Gaikwad, "Design, Development and Performance Evaluation of Fish Descaling Machine", Fishery Technology, 54(4) (2017)
- H. Hong, "Visual quality detection of aquatic products using machine vision", Aquacultural Engineering, 63, pp.62~71 (2014)[DOI]
- M. Kobayashi, "Fish to 2030: the role and opportunity for aquaculture", Aquaculture economics & management, 19(3), pp.282~300 (2015)[DOI]
- G. Sanchez-Torres, "Automatic measurement of fish weight and size by processing underwater hatchery images", Engineering Letters, 26(4), pp.461~472 (2018)
- W. H. Choi, "For the Indirect Measurement of Ocean Acidification, Finding a Plan to Collect Big Data of Crab Catches Using Domestic Fishing Boats: Focusing on the West Sea in South Korea", Proc. Int. Conf. on Multimedia Information Technology and Applications (MITA), pp.458~461 (2021)
- F. Antonucci, "Precision aquaculture: a short review on engineering innovations", Aquaculture International, 28(1), pp.41~57 (2020)[DOI]
- I. Laradji, "Affinity lcfcn: Learning to segment fish with weak supervision", ~ (2020)
- R. JONGJARAUNSUK, "Weight Estimation of Asian Sea Bass (Lates calcarifer) Comparing Whole Body with and without Fins using Computer Vision Technique", Walailak Journal of Science and Technology (WJST), 18(10), pp.9495~ (2021)[DOI]
- T. M. Banhazi, "Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results", Australian Journal of Multi-disciplinary Engineering, 8(2), pp.107~119 (2011)[DOI]
- X. Li, "Study on the Freshness Identification of Frozen-Thaw Fish Based on Hyperspectral Detection Technology", Proc. Int. Conf. on Multimedia Information Technology and Applications (MITA), pp.96~99 (2021)
- I. Nyalala, "On-line weight estimation of broiler carcass and cuts by a computer vision system", Poultry Science, 100(12), pp.101474~ (2021)[DOI]
- A. Pezzuolo, "On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera", Computers and electronics in agriculture, 148, pp.29~36 (2018)[DOI]
- R. A. Hauser-Davis, "Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza)", Ecological Informatics, 5(6), pp.474~478 (2010)[DOI]
- D. Li, "Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish", Artificial Intelligence Review, pp.1~40 (2021)[DOI]
- A. Voulodimos, "Deep learning for computer vision: A brief review", Computational intelligence and neuroscience, 2018 (2018)[DOI]
- T. T. E. Vo, "Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision", Electronics, 10(22), pp.2882~ (2021)[DOI]
- J. Park, "A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network", Electronics, 10(19), pp.2407~ (2021)[DOI]
- S. H. Kim, "Consistency of medical data using intelligent neuron faster R-CNN algorithm for smart health care application", Healthcare, 8(2) (2020), Multidisciplinary Digital Publishing Institute[DOI]
- Y. Lee, "Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network", Sensors, 21(10), pp.3351~ (2021)[DOI]
- S. K. Kumaran, "Computer vision-guided intelligent traffic signaling for isolated intersections", Expert Systems with Applications, 134, pp.267~278 (2019)[DOI]
- Y. J. Choi, "Residual-Based Graph Convolutional Network for Emotion Recognition in Conversation for Smart Internet of Things", Big Data (2021)[DOI]
- J. M. Miranda, "A prototype to measure rainbow trout's length using image processing", Aquacultural engineering, 76, pp.41~49 (2017)[DOI]
- Q, Al-Jubouri, "Towards automated length-estimation of free-swimming fish using machine vision", Proc, IEEE. Int, Conf. on Systems, Signals & Devices (SSD), pp.469~474 (2017)
- M. O. Balaban, "Using image analysis to predict the weight of Alaskan salmon of different species", Journal of food science, 75(3), pp.E157~E162 (2010)[DOI]
- Y. Wu, "Automatic Sorting System of Large Yellow Croaker Based on Machine Vision", Proc. IEEE Int. Conf. on High Performance Big Data and Intelligent Systems (HPBD&IS), pp.233~237 (2019)
- S. Yang, "Design of bay scallop roll grading equipment", Food Research And Development, 37(12), pp.220~224 (2016)
- D. G. Lee, "Testing cost reduction using nested mutation testing", Proc. Int. Conf. on Multimedia Information Technology and Applications (MITA), pp.462~463 (2021)
- K. A. Kamaruzzaman, "Design and performance evaluation of semi-automatic fish cutting machine for industry", IOP Conference Series: Materials Science and Engineering, 864(1) (2020), IOP Publishing
- C. Pornpanomchai, "Shape-and texture-based fish image recognition system", Agriculture and Natural Resources, 47(4), pp.624~634 (2013)
- C. U. Son, "Implementation of the Controller for intelligent Process System Using Neural Network", Proc. Conf. Korean Institute of Intelligent Systems (KIIS), pp.376~379 (2000)
- H. Azarmdel, "Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision", Computers and Electronics in Agriculture, 162, pp.613~629 (2019)[DOI]
- M. Man, "Fish Length Measurement: The Results from Different Types of Digital Camera", Journal of Advanced Agricultural Technologies, 3(1), pp.67~71 (2016)[DOI]
- D. A. Konovalov, "Automatic weight estimation of harvested fish from images", 2019 Digital Image Computing: Techniques and Applications (DICTA), pp.1~7 (2019), IEEE
- 현수환, "동결생선의 외형과 부피 예측 모델링 및 정중량 절단", 한국지능시스템학회 논문지, 22(3), pp.284~299 (2012), 한국지능시스템학회[DOI]
- M. A. Fischler, "Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography", Communications of the ACM, 24(6), pp.381~395 (1981)[DOI]