Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms

Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder–decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation

Detection and Classification of Road Users in Aerial Imagery Based on Deep Neural Networks

Práce se zaobírá problematikou neuronových sítí z pohledu úlohy detekce dopravních prostředků v obraze, který byl pořízen z dronu. Jelikož je cílem vytvořit prakticky použitelný detektor, práce jako první popisuje datovou sadu. Dále práce rozebírá několik architektur dopředných neuronových sítí, které byly následně použity při realizaci detektoru. Na architektury neuronových sítí navazují metody tvorby detektoru pomocí naivních metod a současně nejúspěšnějších meta architektur. V druhé části se práce zabývá praktickou realizací detektoru. Výsledkem práce je detektor postavený na meta architektuře Faster R-CNN a neuronové sítí PVA s úspěšností detekce přes 90 % a rychlostí 45 full HD snímků za sekundu.This master's thesis deals with a vehicle detector based on the convolutional neural network and scene captured by drone. Dataset is described at the beginning, because the main aim of this thesis is to create practicly usable detector. Architectures of the forward neural networks which detector was created from are described in the next chapter. Techniques for building a detector based on the naive methods and current the most successful meta architectures follow the neural network architectures. An implementation of the detector is described in the second part of this thesis. The final detector was built on meta architecture Faster R-CNN and PVA neural network on which the detector achieved score over 90 % and 45 full HD frames per seconds.

Dolphins in Space: Quantifying the Relative Positions of Bottlenose Dolphins (Tursiops truncatus)

Bottlenose dolphins (Tursiops truncatus) are socially sophisticated mammals with high fission-fusion dynamics and complex communication. The relative positioning of individual dolphins as they swim within their social group may aid in the expression of social roles. This study sought to quantify relative positioning in a small social group of female bottlenose dolphins at the National Aquarium in Baltimore that included two mother-daughter pairs, maternal and paternal half-sisters, a half-aunt and niece, and one unrelated female. We devised a method for scoring relative positioning in three dimensions. We found that the two mothers and their juvenile and adult daughters often swam in pairs, indicating that the mother-offspring relationship continued to be an important affiliation later in life. The two dolphins without a mother or daughter in the group, as well as the youngest juvenile female (one of the daughters), spent more time swimming alone than with others. Both of the mother-daughter pairs frequently swam in a position known as the infant position in the literature, despite the fact that both of the daughters in our group were 8 and 13 years of age. Among frequently associating non-mother/daughter pairs, there was some evidence that one dolphin typically stayed in front of the other, possibly indicating leader/follower roles. Conversely, there was no evidence that any dolphin stayed to the left or right of another; to the inside or outside of another in relation to the pool wall; or above or below another. A discussion of the application of developing technologies, such as machine learning techniques and unmanned aerial vehicles, to future research on relative positioning in cetacean social groups is included