| Summary: | 碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 107 === Fish catch statistics reported by vessels are essential information for the management of marine resource. The statistics were conventionally recorded by observers or fishermen. Manual recording is time consuming and can be subjective; thus, there is a demand for automatic statistics collection and reporting. The decks of fishing vessels are usually full of miscellaneous items, making automatic reporting of the statistics challenging. In recent years, convolutional neural networks (CNNs) have become increasingly popular and been applied to solving complex machine vision tasks. This study proposed to automatically identify 11 species or types of fish harvested by longliners using deep CNNs. The species included albacore (Thunnus alalunga), bigeye tuna (T. obesus), yellowfin tuna (T. albacares), southern bluefin tuna (T. maccoyii), blue marlin (Makaira nigricans), Indo-Pacific sailfish (Istiophorus platypterus), swordfish (Xiphias gladius), and dolphin fish (Coryphaena hippurus). Four deep CNNs modified from architectures VGG-16, ResNet-50, DenseNet-201, and MobileNetV2 were trained to identify the species and types of the fish in images collected on longliners. Center loss function was also applied during training for improving the performance of the CNNs. The CNNs reached an accuracy of as high as 95.83% and required a processing time of as short as 1.75 ms using a GPU and 107.82ms using a CPU.
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