Classical and neural network approaches to object detection in underwater robotics competitions

Abstract

The article considers two approaches to detecting underwater objects in the image, i.e. classical approach and neural network approach. Main advantages and disadvantages of each approach are presented. Various approaches to operation quality were analyzed, including assessment of speed and accuracy, as well as identification of preconditions required to achieve the maximum quality. The article also considers preliminary use of image dehazing methods to improve visibility and contrast. Objects for recognition considered in the article are elements of missions in the Singapore Autonomous Underwater Vehicle Challenge (SAUVC) competitions in Singapore. Nvidia Jetson TX2 single-board computer is the target platform for the proposed methods, analysis of the method speed was carried out both using the graphics processing unit (GPU) for neural network, and without using it in classical and neural network methods in order to obtain potential speed estimate on simpler platforms without the GPU.