DeformDANet

Validated vision model counts deformable shrimp larvae with measurable accuracy

Counting deformable shrimp larvae has always been a problem that resists automation. Their bodies flex into irregular shapes, and hundreds cluster together in a single frame, confusing conventional detectors. DeformDANet solves this with geometry-adaptive feature extraction and a density-aware assignment strategy, achieving 94.5% mAP@50 with just 1.38 million parameters. That efficiency matters for real hatchery deployment. The result is a validated, measurable path toward intelligent aquaculture management, complementing our coverage of sustainable practices in "Microalgae-Larvae Carbon Transfer."