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Poster Session 3, 1:15 PM - 2:00 PM: Room 163 [C21]

Presenter: Thuyen Pham

Faculty Sponsor: Jeremy Catalin Andersen

School: UMass Amherst

Research Area: Computer Science

ABSTRACT

In New England, the native Bruce spanworm, Operophtera bruceata, and the invasive Winter Moth, Operophtera brumata, coexist, share similar morphology, and hybridize extensively, thus complicating efforts to assess population dynamics and manage the invasive species’ spread. Traditional identification methods, such as genitalia morphology and DNA-based phylogenetics, are accurate but slow, labor-intensive, and unsuitable for large-scale monitoring. Preliminary evidence shows that wing venation landmarks differ between the two species, suggesting feasible automatic species-classifier based on wing venation. Previous works in vein detection use U-Net and CNN architectures for semantic segmentation, but these models demand dense annotations and intensive multi-layer computation during training and inference. In this project, we investigate a low-annotation alternative using a one-stage detector You Look Only Once (YOLO) on bleached wing images to (i) localize wings and (ii) support landmark-based feature extraction for classification. We train on 2000 images (1000 images per species) and evaluate using stratified split by species. YOLO performance is summarized using mAP@0.5, mAP@0.5:0.95, and F1-score. Ongoing work will compare the result from YOLO model investigation with the result obtained from state-of-the-art U-Net and CNN. We anticipate YOLO will provide the best tradeoff between accuracy and throughput for localizing wings, enabling rapid processing of large image batches. A segmentation model such as U-Net may yield more detailed wing boundaries, but at higher annotation cost and computational complexity.