Automatic weight estimation of harvested fish from images

Konovalov, Dmitry A., Saleh, Alzayat, Efremova, Dina B., Domingos, Jose A., and Jerry, Dean R. (2019) Automatic weight estimation of harvested fish from images. In: Proceedings of the International Conference on Digital Image Computing. pp. 308-314. From: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia.

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Abstract

Approximately 2,500 weights and corresponding images of harvested Lates calcarifer (Asian seabass or barramundi) were collected at three different locations in Queensland, Australia. Two instances of the LinkNet-34 segmentation Convolutional Neural Network (CNN) were trained. The first one was trained on 200 manually segmented fish masks with excluded fins and tails. The second was trained on 100 whole-fish masks. The two CNNs were applied to the rest of the images and yielded automatically segmented masks. The one-factor and two-factor simple mathematical weight-from-area models were fitted on 1072 area-weight pairs from the first two locations, where area values were extracted from the automatically segmented masks. When applied to 1,400 test images (from the third location), the one-factor whole-fish mask model achieved the best mean absolute percentage error (MAPE), MAPE = 4.36%. Direct weight-from-image regression CNNs were also trained, where the no-fins based CNN performed best on the test images with MAPE = 4.28%.

Item ID: 61472
Item Type: Conference Item (Research - E1)
ISBN: 978-1-7281-3857-2
Copyright Information: © 2019 IEEE
Funders: Australian Research Council (ARC), Mainstream Aquaculture
Date Deposited: 22 Jan 2020 00:38
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 100%
SEO Codes: 83 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 8301 Fisheries - Aquaculture > 830102 Aquaculture Fin Fish (excl. Tuna) @ 100%
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