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The device used in this work detects the objects over the surface of thewater using two thermal cameras which aid the users to detect and avoid theobjects in scenarios where the human eyes cannot (night, fog, etc.). To avoidthe obstacle collision autonomously, it is required to track the objects inreal-time and assign a specific identity to each object to determine itsdynamics (trajectory, velocity, etc.) for making estimated collisionpredictions. In the following work, a Machine Learning (ML) approach forComputer Vision (CV) called Convolutional Neural Network (CNN) was used usingTensorFlow as the high-level programming environment in Python. To validate thealgorithm a test set was generated using an annotation tool that was createdduring the work for proper evaluation. Once validated, the algorithm wasdeployed on the platform and tested with the sequence generated by the testboat.

Siamese Network Training Using Artificial Triplets By Sampling and Image Transformation