Non-Max Suppression (NMS)

Object detection models often predict multiple overlapping bounding boxes for the same object. This occurs because models scan an image at different scales and positions, leading to redundant predictions. Non-Max Suppression (NMS) is a crucial post-processing step that refines these detections by selecting the most relevant bounding boxes while discarding unnecessary ones.

Why Multiple Overlapping Boxes Occur

Object detectors predict multiple bounding boxes for a single object due to:

• Anchor boxes: some models, like Faster R-CNN and YOLO, use predefined anchor boxes of different sizes, leading to multiple detections of the same object;
• Sliding window approaches: some detection methods scan an image in small regions, causing repeated detections of an object within overlapping areas;
• High confidence scores: the model assigns confidence scores to each prediction, and similar predictions with high confidence may overlap significantly.

If these redundant boxes are not filtered, they can negatively impact object detection performance by increasing computational complexity and making object counts unreliable.

How Non-Max Suppression Works

NMS is applied to remove duplicate detections while keeping the most confident bounding box. It follows these steps:

• Score Sorting: arrange all predicted bounding boxes in descending order based on their confidence scores;
• Selecting the Best Box: pick the highest-scoring box and add it to the final list of detections;
• IoU Computation: calculate the Intersection Over Union (IoU) between the selected box and all remaining boxes;
• Thresholding: suppress (remove) boxes that have an IoU above a set threshold (e.g., 0.5) since they likely represent the same object;
• Repeat: continue this process for the next highest-scoring box until all boxes are processed.

Soft-NMS: A Smarter Alternative

Standard NMS removes all overlapping boxes above a certain IoU threshold, which may sometimes eliminate useful detections. Soft-NMS improves upon this by decaying the confidence scores of overlapping boxes instead of removing them outright. This method allows for a smoother suppression process and can improve detection performance in dense object scenarios.

Key Differences Between Standard NMS and Soft-NMS:

• Standard NMS: removes overlapping boxes above the IoU threshold;
• Soft-NMS: reduces the confidence scores of overlapping boxes instead of eliminating them, making it more flexible for detecting partially occluded objects.

Balancing Accuracy and Speed in NMS

Choosing the right IoU threshold is crucial:

• Higher thresholds (e.g., 0.6 - 0.7): more aggressive filtering, keeping only the most confident boxes but possibly missing some valid detections;
• Lower thresholds (e.g., 0.3 - 0.4): Retains more overlapping boxes, which can be useful in crowded scenes but may increase false positives.

Using optimized implementations, such as TensorFlow or OpenCV’s built-in NMS functions, can help speed up processing for real-time applications.

Non-Max Suppression is a vital technique in object detection to remove redundant bounding boxes and improve model accuracy. Standard NMS efficiently eliminates duplicate detections, while Soft-NMS refines the process by adjusting confidence scores instead of outright removal. By fine-tuning IoU thresholds, one can strike the right balance between detection accuracy and computational efficiency.