| Summary: | 碩士 === 國立臺灣海洋大學 === 資訊工程學系 === 106 === This paper proposes the use of convolutional neural networks (CNNs) for real-time recognition of numbers on fishing vessels entering and leaving their ports. First, video cameras were mounted at the access of fishing ports to capture images of entries into and exits from these ports. Then, fishing vessels in the images were detected and positions of license plates on the vessels were located. After cutting and trimming, numbers on the fishing vessels were recognized. The recognized fishing vessel numbers then underwent rearrangement of their positions and trust scores of fishing vessel numbers that were recognized in their positions were organized in descending order. Finally, fishing vessel numbers that complied with the fishing vessel numbering rules and had the highest trust scores were regarded as the detected numbers of the fishing vessels. The recognition results were then shown in the video images. After experimentation in multiple networks, the results show that the accuracy is up to 58.3%
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