{"ID":2829433,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12101","arxiv_id":"2512.12101","title":"AI-Augmented Pollen Recognition in Optical and Holographic Microscopy for Veterinary Imaging","abstract":"We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appearances. We establish the performance baseline by training YOLOv8s for object detection and MobileNetV3L for classification on a dual-modality dataset of automatically annotated optical and affinely aligned DIHM images. On optical data, detection mAP50 reaches 91.3% and classification accuracy reaches 97%, whereas on DIHM data, we achieve only 8.15% for detection mAP50 and 50% for classification accuracy. Expanding the bounding boxes of pollens in DIHM images over those acquired in aligned optical images achieves 13.3% for detection mAP50 and 54% for classification accuracy. To improve object detection in DIHM images, we employ a Wasserstein GAN with spectral normalization (WGAN-SN) to create synthetic DIHM images, yielding an FID score of 58.246. Mixing real-world and synthetic data at the 1.0 : 1.5 ratio for DIHM images improves object detection up to 15.4%. These results demonstrate that GAN-based augmentation can reduce the performance divide, bringing fully automated DIHM workflows for veterinary imaging a small but important step closer to practice.","short_abstract":"We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substant...","url_abs":"https://arxiv.org/abs/2512.12101","url_pdf":"https://arxiv.org/pdf/2512.12101v1","authors":"[\"Swarn S. Warshaneyan\",\"Maksims Ivanovs\",\"Blaž Cugmas\",\"Inese Bērziņa\",\"Laura Goldberga\",\"Mindaugas Tamosiunas\",\"Roberts Kadiķis\"]","published":"2025-12-13T00:26:45Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\",\"q-bio.QM\",\"stat.ML\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}