Siamese Network Training Using Artificial Triplets By Sampling and Image Transformation
This work addresses obstacle detection for autonomous navigation in low-visibility conditions like night or fog, but appears incremental as it applies existing methods to a specific domain.
The paper tackles real-time object tracking and identification on water surfaces using thermal cameras to enable autonomous obstacle avoidance, achieving deployment and testing on a real platform.
The device used in this work detects the objects over the surface of the water using two thermal cameras which aid the users to detect and avoid the objects in scenarios where the human eyes cannot (night, fog, etc.). To avoid the obstacle collision autonomously, it is required to track the objects in real-time and assign a specific identity to each object to determine its dynamics (trajectory, velocity, etc.) for making estimated collision predictions. In the following work, a Machine Learning (ML) approach for Computer Vision (CV) called Convolutional Neural Network (CNN) was used using TensorFlow as the high-level programming environment in Python. To validate the algorithm a test set was generated using an annotation tool that was created during the work for proper evaluation. Once validated, the algorithm was deployed on the platform and tested with the sequence generated by the test boat.