plSSN : 0374-8111 | elSSN : 2287-8815
plSSN : 0374-8111 | elSSN : 2287-8815
plSSN : 0374-8111elSSN : 2287-8815
김광일·김병엽1·유상록2·이정훈3·이경훈4*
제주대학교 해양과학대학, 1제주대학교 해양과학대학, 2㈜미래해양정보기술, 3국립수산과학원 수산자원센터, 4부경대학교 해양생산시스템관리학부
In recent years, changes in the fishing ground environment have led to reduced catches by fishermen at traditional fishing spots and increased operational costs related to vessel exploration, fuel, and labor. In this study, we developed a deep learning model to classify the fishing activities of drift gillnet fishing boats using AIS (automatic identification system) trajectory data. The proposed model integrates long short-term memory and 1-dimensional convolutional neural network layers to effectively distinguish between fishing (throwing and hauling) and non-fishing operations. Training on a dataset derived from AIS and validation against a subset of CCTV footage, the model achieved high accuracy, with a classification accuracy of 90% for fishing events. These results show that the model can be used effectively to monitor and manage fishing activities in coastal waters in real time.
Drift gillnet fishing, Fishing activity classification, AIS, Deep learning