Deep Learning Waterline Detection for Low-cost Autonomous Boats
Waterline detection in images captured from a moving camera mounted on an autonomous boat is a complex task, due the presence of reflections, illumination changes, camera jitter, and waves. Localizing the position of water pixels in the camera view is the foundation for building a collision avoidance system that relies on visual data only.
In this work, we present a supervised method for waterline detection, which can be used for low-cost autonomous boats. The method is based on a Fully Convolutional Neural Network for obtaining a pixel-wise image segmentation. Quantitative results show the effectiveness of the proposed approach, with 0.97 accuracy at a speed of 9 frames per second.
Authors: Lorenzo Steccanella, Domenico D. Bloisi, Jason Blum, and Alessandro Farinelli