What is Soft-NMS?
Soft-NMS is an algorithm that improves upon the traditional Non-Maximum Suppression (NMS) method used in object detection. NMS is used to sort detection boxes in order of their scores and eliminate those with a significant overlap with another detection box. Soft-NMS decays the scores of overlapping detection boxes gradually, allowing all objects to remain in the detection process.
Why is NMS used in Object Detection?
In object detection, the goal is to identify objects in an image and produce a bounding box around them. The algorithm used for object detection produces multiple detection boxes, each with a score that indicates the confidence of the object being present in that box. NMS is used to select the best detection box for each object by eliminating the rest of the overlapping detection boxes.
How does NMS work?
NMS works by sorting all detection boxes according to their scores. The detection box with the highest score is selected as the best candidate for a specific object. All other detection boxes with significant overlap with the selected box (using a pre-defined threshold) are then suppressed. This process is recursively applied to the remaining boxes until no more boxes are left to process. The result is a set of detection boxes that contain the best candidates for each object in an image.
What is the problem with NMS?
The traditional NMS method eliminates overlapping objects based on a pre-defined overlap threshold. This means that if an object lies within the threshold, it may be missed by the detection algorithm. Additionally, because the overlap threshold is predefined, it may work well for some images but not for others. In some cases, it can even cause false positives or negatives when detecting objects.
How does Soft-NMS solve this problem?
Soft-NMS solves the problem of NMS missing objects that fall within the pre-defined overlap threshold by gradually decaying the scores of overlapping detection boxes. This decay is performed as a continuous function of the boxes' overlap, meaning that no box is completely eliminated from the detection process. Instead, boxes with high scores receive less decay than boxes with low scores, allowing for all objects to remain in the detection process. This technique can also improve the accuracy of object detection by reducing false positives and negatives.
Soft-NMS is an algorithm that improves upon the traditional Non-Maximum Suppression method by decaying the scores of overlapping detection boxes. This allows all objects to remain in the detection process, reducing the risk of missing objects or producing false positives and negatives. Soft-NMS is a useful tool to improve the accuracy and reliability of object detection.
