Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means
| Autor: | João M. Pereira, Vahid Tarokh, Yuting Ng, Denis Garagic |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 050210 logistics & transportation Battle Buoy Automatic Identification System Computer science media_common.quotation_subject 05 social sciences Fishing k-means clustering Machine Learning (stat.ML) 02 engineering and technology Machine Learning (cs.LG) law.invention West africa Statistics - Machine Learning law 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Dropout (neural networks) media_common Marine engineering |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp40776.2020.9053064 |
| Popis: | Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption. We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%. Comment: ICASSP 2020 |
| Databáze: | OpenAIRE |
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