Development of Animal Morphology Measurement Tool with Convolutional Neural Networks and Single-View Metrology Algorithms

Research aimed at obtaining physical measurements of animals in the wild generally makes use of chemical immobilizers to manipulate the object of study, which can be detrimental to the latter. This is why the present research of quantitative approach performs an experimental study that proposes the union of single-view metrology algorithms with the implementation of convolutional neural networks proposed in the YOLO model to develop a web application with two-layer architecture that can classify and take measurements of animals photographed with monocular camera traps in open spaces. This study returned positive results by allowing the development of a web page capable of taking measurements on 2D images with a margin of error of 0.55 cm in 0.49 s and classifying animals with an effectiveness of 93.85%, thus fulfilling the main objective of the study and contributing to the research gap.