softer nms yolov5

Softer NMS (Soft Non-Maximum Suppression) is a modification of the traditional Non-Maximum Suppression algorithm used in object detection models like YOLOv5. It aims to improve the accuracy of object detection by addressing the issue of overlapping bounding boxes.

In traditional NMS, when multiple bounding boxes overlap, only the one with the highest confidence score is selected, and the rest are suppressed. However, this can lead to the elimination of potentially valid bounding boxes.

Softer NMS introduces a probabilistic approach to handle overlapping bounding boxes. Instead of completely suppressing the lower-scoring boxes, it reduces their confidence scores based on the degree of overlap with higher-scoring boxes. This allows for a more gradual suppression and helps retain more bounding boxes, especially those with lower confidence scores but still represent valid objects.

The main steps involved in Softer NMS are as follows:
1. Sort the bounding boxes based on their confidence scores.
2. Iterate through each bounding box in the sorted order.
3. Update the confidence scores of the remaining bounding boxes based on their overlap with the current box.
4. Apply a decay function to reduce the confidence scores of the overlapping boxes.
5. Repeat steps 3 and 4 until all bounding boxes have been processed.

By using Softer NMS, YOLOv5 can achieve better object detection performance, especially in scenarios where objects are densely packed or have significant overlap.