merged

Autonomous Foreign Object Detection System Using Image Processing
Method for Ship Security
Muhammad Irfan Bin Ishak
Supervisor: Dr. Ahmad Faisal Bin Mohamad Ayob
Bachelor of Applied Science (Maritime Technology)
School of Ocean Engineering
Universiti Malaysia Terengganu
In this study, an autonomous foreign object detection system using Convolutional
Neural Networks (CNN) is presented to classify the threat level of foreign object at sea
with high accuracy rates. Currently, the visual assistance on board is only relies on the
capability of video recording, however lacking in real-time threat identification.
Artificial Neural Networks (ANN) is a proven tool in Machine Learning (ML) for data
classification. Recently, the works in ANN has been extended to better classify images
via training dataset. In this work, we propose a function to identify foreign object and
classify its corresponding threat level. Therefore, the system need to be trained
periodically using new data to improve its accuracy. It is envisaged that through the
use of CNN, accurate threat level of foreign object can be obtained efficiently. Finally
the result which will confirm that this model has the potential for successful function
to classify the foreign object threat with high accuracy rates shall be presented.
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