Automatic Whale Detection using Deep learning

Abstract

Accurate monitoring of whale populations is essential for conservation efforts, yet traditional surveying methods are often time-consuming, expensive, and limited in coverage. This thesis investigates the automation of whale detection using state-of-the-art (SOTA) deep learning techniques applied to high-resolution aerial imagery. By leveraging advancements in computer vision, specifically object detection models, this research aims to develop a robust and efficient system for identifying and counting whales from aerial surveys. The study formulates whale detection as a small object detection problem and evaluates the performance of various SOTA models, including Faster R-CNN, YOLOv8, and Deformable DETR, paired with modern backbone architectures such as ConvNext-T, Swin-T, and ResNet-50. The influence of input image size and context on model performance is systematically explored by testing patch sizes ranging from 256 to 4096 pixels, marking this study as the first to investigate the efficacy of such large patch sizes in the remote sensing domain.

Results indicate that the Faster R-CNN model with a ConvNext-T backbone achieves the highest detection accuracy, with an average precision of 0.878 at an IoU threshold of 0.1, particularly when trained on larger patch sizes. The study also addresses the challenge of domain adaptation by implementing an active learning framework, designed to enhance model performance on new survey data with varying environmental conditions. A novel portfolio-based acquisition function, leveraging the social behavior of whales, is introduced to optimize the annotation process. This research significantly contributes to the field of automated whale monitoring, offering a scalable and adaptable solution that reduces annotation costs and improves the accuracy of population estimates. The developed system holds promise for enhancing conservation strategies and providing valuable insights into whale movements and behaviors.