11 April 2018 Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine
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Abstract
The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab’s size, orientation, and the position of the crab’s meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle’s small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab’s back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Dongyi Wang, Robert Vinson, Maxwell Holmes, Gary Seibel, Yang Tao, "Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine," Optical Engineering 57(4), 043103 (11 April 2018). https://doi.org/10.1117/1.OE.57.4.043103 Submission: Received 29 November 2017; Accepted 21 March 2018
Submission: Received 29 November 2017; Accepted 21 March 2018
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