Implementación de un detector de coral utilizando filtros Gabor Wavelets y máquinas de aprendizaje

Abstract

This work focuses on the implementation of a fast coral reef detector that is used for an Autonomous Underwater Vehicle (AUV, its acronym in English). A fast detection of the presence of coral ensures the AUV stabilization in front of coral reef in the shortest possible time, avoiding collisions with coral. The coral detection is carried out on an image that captures the scene that the AUV’s camera perceives. A pixel-by-pixel classification is performed between two classes: coral reef and the background that is non-coral reef. Each pixel of the image is assigned to a feature vector, which is generated by using Gabor Wavelet filters. These are implemented in C++ and the OpenCV library. The feature vectors are classified using nine machine learning algorithms. The performance of each algorithm is compared with the accuracy and execution time. The Decision Tree algorithm proved to be the fastest and most accurate of all the algorithms. We created a database of 621 images of coral reefs in Belize (110 of training images and 511 of testing images).