Paint Blob Detection and Decoding for Identification of Honey Bees

Andrea P. Gómez-Jaime, Luke Meyers, Josué A. Rodríguez-Cordero, José L. Agosto-Rivera, Tugrul Giray, Rémi Mégret

Abstract: This paper evaluates a new method for the automated re-identification of honey bees marked with paint codes using fewer annotations than previous methods. Monitoring honey bees and understanding their biology can benefit from studies that measure traits at the individual level, requiring methods for re-identification. Marking with colored paint is one method used by biologists for re-identification in the field because it is noninvasive and readable by humans. This work uses the YOLOv8 object detection approach to detect and classify colored paint markings. A new algorithm to decode the identity based on bi-color left/right paint code is proposed. The proposed approach was evaluated on an extensive dataset with 64 distinct color code identities composed of combinations of 8 different colors, with the test set featuring over 4000 images of 64 unseen individuals. The proposed approach reached 93% top-1 accuracy in the recognition of 1 vs 64 identities, achieving better performance than