Abstract
New Zealand maintains excessive effort to organise the sustainable development of its marine resources, wildlife, and ecological environment. New Zealand has stringent rules to control fishing and to protect the continued growth of marine inhabitants. Fishing inspections, such as identifying and counting shellfish, are part of the daily routine of many New Zealand Fisheries officers. It is however considered labour-intensive and time-consuming work. This project, thus, develops a touch-less shellfish detection and counting web/mobile application on handheld devices using Mask R-CNN to assist New Zealand Fisheries officers in recognising and totalling shellfish automatically and accurately. New Zealand shellfish species are different from other places in the World. Thus, this study firstly investigates the best deep learning model to use for New Zealand shellfish recognition and detection. Selected shellfish dataset is collected from a local fish market in Auckland and trained by using the chosen artificial neural network. At last, a portable system is built to support Fisheries officers to count shellfish quickly and accurately. At this current stage, a web-based application has been successfully deployed at a local server (cvreact.aut.ac.nz) in which users can upload target objects to get results related to three major shellfish species including cockle, tuatua, and mussel. In the near future, this proposed model is scaled up to recognise more species to cover the popular shellfish species in New Zealand, thus benefiting the aquaculture as well.
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Nguyen, Q., Nguyen, M., Sun, B., Le, H. (2021). New Zealand Shellfish Detection, Recognition and Counting: A Deep Learning Approach on Mobile Devices. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_10
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DOI: https://doi.org/10.1007/978-3-030-72073-5_10
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