Development of Fishing Vessel Identification Model Based on Deep Neural Network

Ching Hai Lin, Chun Cheng Lin, Ren Hao Chen, Cheng Yu Yeh*, Shaw Hwa Hwang


研究成果: Article同行評審


This paper presents a deep neural network (DNN)-based model to recognize fishing vessels. In Taiwan, the vast majority of small fishing vessels are not equipped with an automatic identification system (AIS). As a consequence, the staff in a fishing port administration become heavily loaded when monitoring and managing the fishing vessels accessing a port. The workload is expected to be eased using this work. For the first time in the literature, a captured fishing vessel image was converted to a 128-dimensional embedding for recognition purposes. The presented model gave a false positive rate (FPR) as low as 1.13% and an accuracy up to 99.47% at threshold = 0.772379. Finally, all the performance metrics, namely, the true positive rate (TPR), the FPR, precision and accuracy, are actually functions of the threshold which can be specified by users to meet specific requirements.


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