I Tripled My YOLO Detection - Without Retraining

A technique has been developed that improves object detection accuracy without retraining YOLO models, particularly in crowded spaces. By adjusting confidence thresholds based on bounding box size and using secondary evidence like keypoints, developers can significantly increase detection rates for small or distant objects. This approach offers a faster alternative to traditional methods but requires careful calibration for different environments.

Read the full article at Towards AI - Medium

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