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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
JournalIEEJ Transactions on Electrical and Electronic Engineering
DOIs
StateAccepted/In press - 2022

Keywords

  • deep learning
  • deep neural network (DNN)
  • fishing vessels identification
  • image recognition

Fingerprint

Dive into the research topics of 'Development of Fishing Vessel Identification Model Based on Deep Neural Network'. Together they form a unique fingerprint.

Cite this