Description: Non-Maximum Suppression (NMS) is a fundamental algorithm in the field of object detection and computer vision. Its main function is to select the best bounding box among multiple proposals that may overlap in the detection of the same object. In practice, when an object detection model generates several boxes indicating the presence of an object, NMS helps filter these proposals by eliminating those that have a high overlap with others, keeping only the most reliable one. This is achieved by calculating an Intersection over Union (IoU) threshold, which determines the degree of overlap between the boxes. If the IoU of two boxes exceeds a predefined threshold, the box with the lowest confidence score is discarded. This process not only improves detection accuracy but also optimizes system performance by reducing the number of redundant detections. NMS is especially relevant in various applications within the field of computer vision, where precise object identification is essential for decision-making.<\/p>\n","protected":false},"excerpt":{"rendered":"
Description: Non-Maximum Suppression (NMS) is a fundamental algorithm in the field of object detection and computer vision. Its main function is to select the best bounding box among multiple proposals that may overlap in the detection of the same object. In practice, when an object detection model generates several boxes indicating the presence of an […]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"glossary-categories":[12140],"glossary-tags":[13096],"glossary-languages":[],"class_list":["post-260477","glossary","type-glossary","status-publish","hentry","glossary-categories-computer-vision-en","glossary-tags-computer-vision-en"],"post_title":"NMS (Non-Maximum Suppression) ","post_content":"Description: Non-Maximum Suppression (NMS) is a fundamental algorithm in the field of object detection and computer vision. Its main function is to select the best bounding box among multiple proposals that may overlap in the detection of the same object. In practice, when an object detection model generates several boxes indicating the presence of an object, NMS helps filter these proposals by eliminating those that have a high overlap with others, keeping only the most reliable one. This is achieved by calculating an Intersection over Union (IoU) threshold, which determines the degree of overlap between the boxes. If the IoU of two boxes exceeds a predefined threshold, the box with the lowest confidence score is discarded. This process not only improves detection accuracy but also optimizes system performance by reducing the number of redundant detections. NMS is especially relevant in various applications within the field of computer vision, where precise object identification is essential for decision-making.","yoast_head":"\n