Non Maximum Suppression

Non-maximum suppression (NMS) is a crucial post-processing step in object detection, aiming to eliminate redundant bounding boxes predicted by object detection models and improve accuracy. Current research focuses on developing faster and more accurate NMS algorithms, including graph-theory based approaches and methods that integrate NMS with other aspects of the detection pipeline, such as confidence calibration or even eliminating NMS altogether through novel training strategies. These advancements are significant because efficient and reliable NMS is critical for real-time object detection applications, impacting fields like autonomous driving, medical image analysis, and robotics.