Ant Detective: An Automated Approach for Counting Ants in Densely Populated Images and Gaining Insight into Ant Foraging Behavior

Abstract

Ant foraging behavior is essential to understanding ecological dynamics and developing effective pest management strategies, but quantifying this behavior is challenging due to the labor-intensive nature of manual counting, especially in densely populated images. This study presents an automated approach using computer vision to count ants and analyze their foraging behavior. Leveraging the YOLOv8 model, the system was calibrated and evaluated on datasets encompassing various imaging scenarios and densities. The study results demonstrate that the system achieves average precision and recall of up to 87.96% and 87,78%, respectively, with only 64 calibration images provided when the both calibration and evaluation images share similar imaging backgrounds. When the background is more complex than the calibration images, the system requires a larger calibration set to generalize effectively, with 1,024 images yielding the precision and recall of up to 83.60% and 78.88, respectively. In more challenging scenarios where more than one thousand ants are present in a single image, the system significantly improves detection accuracy by slicing images into smaller patches, reaching a precision and recall of 77.97% and 71.36%, respectively. The system's ability to generate heatmaps visualizes the spatial distribution of ant activity over time, providing valuable insights into their foraging patterns. This spatial-temporal analysis enables a more comprehensive understanding of ant behavior, which is crucial for ecological studies and improving pest control methods. By automating the counting process and offering detailed behavioral analysis, this study provides an efficient tool for researchers and pest control professionals to develop more effective strategies.