Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.     

Mechanical,Thermal and Microstructural Characteristic of 3D Printed Polylactide Composites with Natural Fibers: Wood,Bamboo and Cork

Journal of Polymers and the Environment - In the study, polylactide-based (PLA) composites modified with natural particles (wood, bamboo, and cork) and with different levels of infilling (100%,...