Absolutely fascinating! With the new Region Counting feature in YOLO11, have you found any performance differences or challenges when comparing small, dense regions to larger, sparse ones? Also, do you think this kind of powerful object counting could be misused, say, for overly-invasive surveillance, and how should the AI community address such ethical concerns?
Great questions! For performance differences, smaller, dense regions might challenge the model with overlapping objects, whereas larger, sparse regions typically allow for clearer detections. YOLO11's advancements in feature extraction help manage these scenarios effectively. For ethical concerns like surveillance, it's crucial for the AI community to prioritize transparency, consent, and regulatory compliance. For more insights, check out our Region Counting guide docs.ultralytics.com/guides/region-counting/.
Absolutely fascinating! With the new Region Counting feature in YOLO11, have you found any performance differences or challenges when comparing small, dense regions to larger, sparse ones? Also, do you think this kind of powerful object counting could be misused, say, for overly-invasive surveillance, and how should the AI community address such ethical concerns?
Great questions! For performance differences, smaller, dense regions might challenge the model with overlapping objects, whereas larger, sparse regions typically allow for clearer detections. YOLO11's advancements in feature extraction help manage these scenarios effectively. For ethical concerns like surveillance, it's crucial for the AI community to prioritize transparency, consent, and regulatory compliance. For more insights, check out our Region Counting guide docs.ultralytics.com/guides/region-counting/.