Near Infrared Imaging and Image Pre-Processing to Improve the Automatic Detection of Canada Geese

Abstract

Migratory shorebirds populations are adversely affected by climate change and loss of habitat thus careful monitoring of their populations is important for early detection of population loss. Current counting methods generally rely on intrusive and time-consuming manual identification. This work is part of a larger project to develop automated classification and counting methods using a remotely piloted aircraft system (RPAS). In addition to the use of RPAS, this work will also investigate if near-infrared (NIR) imaging captured by the RPAS yields detection improvements. Healthy vegetation reflects NIR wavelengths of light which can potentially create a greater contrast between an object and the surrounding vegetation. Preprocessing NIR raw images to enhance the contrast between vegetation and Canada geese (Branta canadensis) to improve object detection using the convolutional neural network (CNN) YOLOv4-Tiny have been investigated in this study. Training was done on a small dataset (423 and 1,269 images), a large dataset (2,000 images), artificially generated dataset (2,000 images) and hybrid datasets consisting of real and artificially generated images of Canada geese (4,000 and 5,000 images). A RPAS was used to obtain greyscale and NIR test images of geese decoys using the RPAS onboard camera and a NIR specific camera at varying altitudes. The NIR preprocessed ground test images showed detection improvements in both number of detections and confidence score percentages when validated against the YOLOv4-Tiny detector that was trained on an augmented dataset and the hybrid datasets. However, the greyscale aerial test images generally outperformed the NIR pre-processed images.

Deep Learning and Synthetic Imagery for Migratory Bird Species Identification Using Drones

“…The collaboration between the Molson Foundation and Carleton University, initiated in 2019, have already yielded two Masters theses by Brendan Ooi (Ooi, 2020) and Jacqueline Szeto (Szeto, 2022) in Carleton University: the first, introducing a general framework for computer vision in UAS bird surveying and the latter using object detection models and near infrared imaging for locating birds. The work presented in this present thesis is a continuation of their work, and the prior contributions of each member in the team will be made clear in the following chapters.…”