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Machine Vision Guided Robotics for Blue Crab Disassembly ---Deep Learning Based Crab Morphology Segmentation
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1800570.(doi:10.13031/aim.201800570)
Authors: Dongyi Wang, Maxwell Holmes, Robert Vinson, Gary Seibel, Yang Tao
Keywords: Automatic Control, Autoencoder, Deep Learning, Food, Machine Vision
University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD, 20740
Corresponding Author: email@example.com
Abstract. The Atlantic Blue Crab is amongst the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. There is a great potential market for vision guided intelligent machines to automate the meat picking process. Understanding crab morphologies in digital crab images is the first and key step for the intelligent machine. To achieve the goal, a deep learning architecture is integrated into the system, and the fully automatic model can segment crab images into five region of interests in single step with high accuracy and efficiency. Compared to the pervious knuckle detection algorithm proposed by our group, the updated model can accurately locate not only knuckle positions, but also crab legs and crab cores. The average pixel accuracy can get up to 0.9843. From another angle, the computation time of the updated model decreases 50 folds compared to the pervious method. It can further improve the processing speed of the crab machine. The image segmentation results can be used for generating crab cutlines in XY plane, determining starting cutting points in Z plane, and guiding end effectors to harvest crab meat. This work promises a bright further in not only crab industry but also other natural resources meat picking area.
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