Transfer learning-based approach to individual Apis cerana segmentation - Panadda Kongsilp; Unchalisa Taetragool; Orawan Duangphakdee (PLoS ONE, 2025)
Honey bees play a crucial role in natural ecosystems, mainly through their pollination services. Within a hive, they exhibit intricate social behaviors and communicate among thousands of individuals. Accurate detection and segmentation of honey bees are crucial for automated behavior analysis, as they significantly enhance object tracking and behavior recognition by yielding high-quality results. This study is specifically centered on the detection and segmentation of individual bees, particularly Apis cerana, within a hive environment, employing the Mask R-CNN deep learning model. We used transfer learning weights from our previously trained Apis mellifera model and explored data preprocessing techniques, such as brightness and contrast enhancement, to enhance model performance. Our proposed approach offers an optimal solution with a minimal dataset size and computational time while maintaining high model performance. Mean average precision (mAP) served as the evaluation metric for both detection and segmentation tasks. Our solution for A. cerana segmentation achieves the highest performance with a mAP of 0.728. Moreover, the number of training and validation sets was reduced by 85% compared to our previous study on the A. mellifera segmentation model.