Anchor Boxes & Non-Max Suppression: Perfecting Object Detection

Object detection, a pivotal aspect of computer vision, seeks to identify and locate objects within images or video streams. With the advent of deep learning, the field has seen remarkable advancements, yet two challenges persist: overlapping objects and the suppression of redundant detections. This is where Anchor Boxes and Non-Maximum Suppression (NMS) come into play. These two techniques enhance object detection models, such as YOLO (You Only Look Once) and Faster R-CNN, allowing them to deal effectively with these challenges. This blog post will provide an overview of both anchor boxes and NMS.

1. Anchor Boxes

The use of anchor boxes has become an essential part of detecting objects in contemporary models, but why? To understand this, we first need to take a quick detour into the basics of object detection.

In object detection, an image is divided into a grid, and each grid cell is responsible for predicting objects within it. However, a grid cell can detect only one object. So, what happens when multiple objects are located within a single grid cell?

This is where anchor boxes come into the rescue. Anchor boxes are pre-defined bounding boxes with a certain height and width. These boxes are designed based on the shape and size of the objects we want our model to detect. Instead of allowing each grid cell to predict only one object, we now allow it to predict one object for each anchor box.

So, if a grid cell has three anchor boxes, it can detect up to three objects. The anchor box with the highest IoU (Intersection over Union) score with the ground truth box will be responsible for detecting an object. Therefore, anchor boxes enable us to detect multiple objects that have been assigned to the same grid cell.

2. Non-Maximum Suppression

Non-Maximum Suppression (NMS) is another crucial technique in object detection, used after the prediction step. Despite the effectiveness of modern object detectors, they often predict multiple bounding boxes for the same object. These redundant detections need to be eliminated to have the final bounding box for each detected object.

Non-Maximum Suppression operates in the following steps:

1. Step 1: All the predicted bounding boxes with their confidence scores are taken as input.

2. Step 2: The bounding box with the highest confidence score is selected as the "anchor box" and all other bounding boxes with a significant overlap (i.e., high IoU score) with this box are suppressed.

3. Step 3: The above steps are repeated until all bounding boxes are either selected or suppressed.

By this process, NMS ensures that each object is represented by one bounding box, specifically the one with the highest confidence score.

Combining Anchor Boxes and Non-Max Suppression

The combination of anchor boxes and non-maximum suppression creates a powerful and efficient object detection system. Anchor boxes allow for the detection of multiple objects within each grid cell, and non-maximum suppression ensures that each object is detected only once by removing redundant bounding boxes. These techniques have dramatically improved the precision and reliability of object detection models, making them indispensable tools in the computer vision toolkit.

In conclusion, the future of object detection lies in the continuous improvement and fine-tuning of such techniques, bringing us ever closer to the goal of enabling computers to "see" and understand visual data just as humans do.