mAP@50
3 stories filed under mAP@50 on World Data Ocean. The newest of them: “Counting shrimp with calibrated precision through enhanced acoustic detection”, “Validated vision model counts deformable shrimp larvae with measurable accuracy”, and “Advancing Underwater Plastic Detection with Integrated Data and AI.”. Counting shrimp with calibrated precision through enhanced acoustic detection is now measurable. Counting deformable shrimp larvae has always been a problem that resists automation. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to… The list below is every mAP@50 story on World Data Ocean, newest first.
Counting shrimp with calibrated precision through enhanced acoustic detection
Counting shrimp with calibrated precision through enhanced acoustic detection is now measurable. Small size, dense aggregation, and background clutter have long defeated conventional models. Our improved RT-DETR framework, integrating Efficient Multi-Scale Attention, a dedicated P2 detection layer, and combined Focal-WIoU loss, achieves a mean mAP@50 of 0.844. Paired with DeepSORT tracking, automated counting reaches 83.32% accuracy across video clips. This is a validated, empirical step toward real-world intake safety monitoring.
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."
Advancing Underwater Plastic Detection with Integrated Data and AI.
Underwater plastic debris is small, low-contrast, and easily lost in visually cluttered environments. That is why the improved RF-DETR detector developed here matters. By integrating frequency-aware feature reweighting with adaptive query strategies, the model sharpens weak-texture details and boosts localization precision. On the TrashCan dataset, it reached 93.11% precision, and its transfer to DeepTrash shows strong cross-dataset generalization. The results are practical, measurable, and directly relevant to monitoring efforts.