Using YOLOv8 and Active Contour Models to Detect and Segment Ladybird Beetles in Natural Environments

Ladybird beetles, also known as ladybugs, are a diverse family of small, brightly colored beetles with thousands of species around the world. Their geographic distribution and their impacts as invasive species on other endemic populations are still not well understood. The ability to accurately identify and study ladybird beetles is essential for effective management and conservation efforts. In this paper, we propose a novel method for the detection and segmentation of ladybird beetles in natural environments. Our approach combines the YOLOv8 object detection model and the "Snakes" active contour model to achieve precise detection and segmentation of ladybird beetles. We evaluated our method on a data set of 2300 ladybird beetle images from the iNaturalist project, obtaining a DICE score of 84.82% and an IoU score of 73.73%. These results demonstrate the effectiveness of our approach in accurately identifying and segmenting ladybird beetles. Our method has potential applications in biodiversity research, invasive species management, and ecological monitoring.