Morphological and Optical Characterization of Colored Nanotubular Anodic Titanium Oxide Made in an Ethanol-Based Electrolyte.

In this paper, the possibility of color controlling anodic titanium oxide by changing anodizing conditions of titanium in an ethanol-based electrolyte is demonstrated. Colored anodic titanium oxide was fabricated in an ethanol-based electrolyte containing 0.3 M ammonium fluoride and various amounts of deionized water (2, 3.5, 5, or 10 vol%), at voltages that varied from 30 to 60 V and at a constant anodization temperature of 20 °C. Morphological characterization of oxide layers was established with the use of a scanning electron microscope. Optical characterization was determined by measuring diffusion reflectance and calculating theoretical colors. The resulting anodic oxides in all tested conditions had nanotubular morphology and a thickness of up to hundreds of nanometers. For electrolytes with 3.5, 5, and 10 vol% water content, the anodic oxide layer thickness increased with the applied potential increase. The anodic titanium oxide nanotube diameters and the oxide thickness of samples produced in an electrolyte with 2 vol% water content were independent of applied voltage and remained constant within the error range of all tested potentials. Moreover, the color of anodic titanium oxide produced in an electrolyte with 2 vol% of water was blue and was independent from applied voltage, while the color of samples from other electrolyte compositions changed with applied voltage. For samples produced in selected conditions, iridescence was observed. It was proposed that the observed structural color of anodic titanium oxide results from the synergy effect of nanotube diameter and oxide thickness.

Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods.

Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU's financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study reports on the automatic detection of small inflatable boats and people in a rough wild terrain in the infrared thermal domain. Three acquisition campaigns were carried out during spring, summer, and fall under various weather conditions. Three deep learning algorithms, namely, YOLOv2, YOLOv3, and Faster R-CNN working with six different feature extraction neural networks were trained and evaluated in terms of performance and processing time. The best performance was achieved with Faster R-CNN with ResNet101, however, processing requires a long time and a powerful graphics processing unit.