The non-maximum suppression (NMS) algorithm filters out redundant predicted bounding boxes based on their confidence scores and spatial overlap. For a given predicted bounding box $$B$$, its confidence is the largest predicted class likelihood, denoted as $$p$$. First, all non-background bounding boxes on an image are sorted by confidence in descending order to form a list $$L$$. The algorithm iteratively selects the bounding box $$B_1$$ with the highest confidence in $$L$$ as a basis and removes any remaining boxes in $$L$$ whose Intersection over Union (IoU) with $$B_1$$ exceeds a predefined threshold $$\epsilon$$. Those with non-maximum confidence scores are thus suppressed. This process is repeated using the next highest confidence box as the new basis until every box in $$L$$ has been used as a basis, resulting in a final set of predictions where no two boxes have an IoU greater than $$\epsilon$$.

Non-Maximum Suppression Algorithm

The region proposal network (RPN) is an internal component of Faster R-CNN that generates region proposals by processing CNN-extracted feature maps in four steps. First, a $$3 	imes 3$$ convolutional layer with a padding of $$1$$ transforms the feature maps into a new output with $$c$$ channels, providing a length-$$c$$ feature vector for each spatial unit. Second, multiple anchor boxes of varying scales and aspect ratios are generated and centered on each pixel. Third, these length-$$c$$ feature vectors are used to predict a binary class (object or background) and a bounding box for each anchor box. Finally, non-maximum suppression is applied to the bounding boxes predicted as objects to remove overlapping results, yielding the final region proposals required by the region of interest pooling layer.

Region Proposal Network

When an object detection model generates numerous anchor boxes, it often outputs multiple predicted bounding boxes that surround the same underlying object, leading to significant overlap and redundancy. To simplify the output and ensure each object is detected only once, a technique known as non-maximum suppression (NMS) is employed. NMS merges or suppresses similar predicted bounding boxes that belong to the same object by retaining only the highest-confidence predictions and discarding the overlapping duplicates.

Claude

At inference time, an anchor-box-based object detection model follows a multi-step prediction pipeline for each input image. First, the model generates multiple anchor boxes at various positions and scales across the image. Next, it predicts a class label and a set of bounding-box offsets for every anchor box simultaneously. The predicted offsets are then applied to adjust the positions and sizes of the corresponding anchor boxes, producing a set of predicted bounding boxes. Finally, a filtering step retains only those predicted bounding boxes that satisfy certain criteria (such as confidence thresholds or non-maximum suppression), which are output as the final detections.

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