YOLOv8E: an efficient YOLOv8 method for instance segmentation of individual tree crowns in Wellington City, New Zealand

Instance segmentation is crucial for analysing individual tree crowns in aerial imagery, which plays an important role in forest management, risk modelling, biodiversity modelling, forest health and human wellbeing studies. Traditional instance segmentation methods struggle in diverse rural landscapes where canopy images feature primarily small and medium tree objects, varying from isolated trees to dense forest stands. This paper introduces YOLOv8E, a new and efficient YOLOv8 method, optimised for precise instance segmentation and species classification of tree crowns. This method includes new schemes for selecting candidate positive samples for each instance and a refined network design tailored for small and medium-sized tree crowns. Adjustments in hyperparameters, particularly within the Task-Aligned Assigner, are also discussed to better suit canopy segmentation tasks. Comprehensive experiments conducted on the datasets for Wellington City, Aotearoa New Zealand, demonstrate that YOLOv8E outperforms a number of recent methods, achieving 36.1 and 32.2 in terms of the Box AP and Mask AP metrics respectively.