Non-Maximum Suppression (NMS) is a crucial algorithm used primarily in the field of Computer Vision, particularly in object detection tasks. The main objective of NMS is to eliminate redundant overlapping bounding boxes that may be predicted for the same object in an image, thereby simplifying the results and improving the quality of the detections.
When an object detection model processes an image, it may propose multiple bounding boxes for the same object, each with an associated confidence score indicating how likely it is that the box contains the object. NMS operates by first sorting these bounding boxes based on their confidence scores in descending order. The algorithm then iteratively selects the bounding box with the highest score and removes all other boxes that have a significant overlap with it, measured by the Intersection over Union (IoU) metric. The overlap threshold is a predefined parameter that determines what constitutes ‘significant overlap’.
This process continues until all boxes have been either selected or discarded, resulting in a final set of bounding boxes that are less likely to contain duplicates. By applying NMS, the output is cleaner and more accurate, allowing for better performance in downstream tasks such as image classification and scene understanding.
NMS is widely used in various applications, including facial recognition, autonomous vehicles, and surveillance systems, and is often integrated into larger frameworks for object detection, such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector).