Machine Learning Techniques for Intrusion Detection of Fishermen and Trespassing into Foreign Seas

Abstract

Issues regarding trespassing and intrusion of fishermen in the maritime boundary line is of great importance to be discussed nowadays. One of the main reasons still existing is transgression for better catch of fishes in foreign waters. Thus is a concern, and in order to prevent this issue from becoming a national security threat, it should be taken care of, by identifying the intruders as the first step to get a better view on the situation. Finally, in the hope to slim the chances of transgressions by marine fisher folk, a SVM model based on Automated Identification System that makes use of real-world data is implemented that will analyse the possibility of successful detection of intrusions of fisherman by categorising the vessel as normal or anomalous one. Convolution Neural Network model is used to find whether it is ship or not a ship, and if it is ship then it will categorize whether it belongs to anomalous or non-anomalous. The model's validation accuracy of 96% shows that it can correctly identify whether a ship is present in each image.