Detecting and Tracking of Humans in an Underwater Environment Using Deep Learning Algorithms

Context: The context of this research is to detect and track humans in an underwater environment using deep learning algorithms which can, in turn, reduce the deaths caused due to accidental drowning. Objectives: This study first investigates to find the suitable deep learning algorithms that can be used to detect objects and then an experiment is performed with the chosen algorithms to state the possibility to detect humans in an underwater environment and then evaluate the performance of algorithms. Methods: Firstly, a Literature review is used to find suitable deep learning algorithms and then based on findings an experiment is performed to evaluate the chosen deep learning algorithms. Results: Results from the literature review showed evidence that Faster RCNN and SSD are suitable algorithms and the experimental results showed that Faster RCNN performed better than SSD in detecting humans in an underwater environment. Conclusions: Analyzing the results obtained and considering the real world scenario this thesis is aiming at, it can be concluded that Faster RCNN is the algorithm of choice to detect and track humans in an underwater environment