Lu, YCYCLuTung, CCTungKuo, YFYFKuoYAN-FU KUO2020-11-122020-11-1220201054-3139https://scholars.lib.ntu.edu.tw/handle/123456789/520871Fish catch species provide essential information for marine resource management. Some international organizations demand fishing vessels to report the species statistics of fish catch. Conventionally, the statistics are recorded manually by observers or fishermen. The accuracy of these statistics is, however, questionable due to the possibility of underreporting or misreporting. This paper proposes to automatically identify the species of common tuna and billfish using machine vision. The species include albacore (Thunnus alalunga), bigeye tuna (Thunnus obesus), yellowfin tuna (Thunnus albacares), blue marlin (Makaira nigricans), Indo-pacific sailfish (Istiophorus platypterus), and swordfish (Xiphias gladius). In this approach, the images of fish catch are acquired on the decks of fishing vessels. Deep convolutional neural network models are then developed to identify the species from the images. The proposed approach achieves an accuracy of at least 96.24%. © International Council for the Exploration of the Sea 2019. All rights reserved.convolutional neural network; deep learning; fish species identification; fishery management; model visualization; transfer learning[SDGs]SDG14artificial neural network; finfish; fish; fishery management; fishing vessel; harvesting; identification method; marine resource; tuna fishery; Istiophorus platypterus; Makaira nigricans; Scombridae; Thunnus alalunga; Thunnus albacares; Thunnus obesus; Xiphias gladius; Xiphiidaejournal article10.1093/icesjms/fsz089WOS:000582718700006