What is Non-Max Merging?

Double detection is a common issue in object detection where the same object is detected multiple times, often addressed through Non-Max Suppression (NMS), which retains the most confident detection and discards the rest. An alternative approach, Non-Max Merging (NMM), merges overlapping detections for a consolidated result. Double detections can arise from overlapping predictions by models like YOLO or Faster R-CNN due to anchor boxes or from intentional overlaps by tools like InferenceSlicer. While NMS is faster and generally the default choice, NMM offers a more precise approach for handling overlaps, especially when high-confidence detections require expanded detection areas. Both methods, accessible via the Supervision library, utilize Intersection-Over-Union (IOU) calculations to assess overlap and are applied based on specific use cases and performance considerations.