Anchor Boxes

Anchor boxes are a fundamental concept in modern object detection models such as Faster R-CNN and YOLO. They serve as predefined reference boxes that help detect objects of different sizes and aspect ratios, making detection faster and more reliable.

What Is an Anchor Box?

An anchor box is a predefined bounding box with a fixed size and aspect ratio, placed at specific positions across an image. Instead of detecting objects from scratch, models use anchor boxes as starting points, adjusting them to better fit detected objects. This approach improves efficiency and accuracy, especially for detecting objects of varying scales.

Difference Between Anchor Box and Bounding Box

• Anchor Box: a predefined template that acts as a reference during object detection;
• Bounding Box: the final predicted box after adjustments are made to an anchor box to match the actual object.

Unlike bounding boxes, which are dynamically adjusted during prediction, anchor boxes are fixed at specific positions before any object detection occurs. Models learn to refine anchor boxes by adjusting their size, position, and aspect ratio, ultimately transforming them into final bounding boxes that accurately represent detected objects.

How a Network Generates Anchor Boxes

Anchor boxes are not applied directly to an image but rather to feature maps extracted from the image. After feature extraction, a set of anchor boxes is placed on these feature maps, varying in size and aspect ratio. The choice of anchor box shapes is crucial and involves a balance between detecting small and large objects.

To define anchor box sizes, models typically use a mix of manual selection and clustering algorithms like K-Means to analyze the dataset and determine the most common object shapes and sizes. These predefined anchor boxes are then applied at different locations across the feature maps. For example, an object detection model may use anchor boxes of sizes (16x16), (32x32), (64x64), with aspect ratios such as 1:1, 1:2, and 2:1.

Once these anchor boxes are defined, they are applied to feature maps, not the original image. The model assigns multiple anchor boxes to each feature map location, covering different shapes and sizes. During training, the network adjusts the anchor boxes by predicting offsets, refining their size and position to better fit objects.

From Anchor Box to Bounding Box

Once anchor boxes are assigned to objects, the model predicts offsets to refine them. These offsets include:

• Adjusting the box's center coordinates;
• Scaling the width and height;
• Shifting the box to better align with the object.

By applying these transformations, the model converts anchor boxes into final bounding boxes that closely match the objects in an image.

Approaches That Don't Use Anchors or Reduce Their Number

While anchor boxes are widely used, some models aim to reduce reliance on them or eliminate them entirely:

• Anchor-Free Methods: models like CenterNet and FCOS predict object locations directly without predefined anchors, reducing complexity;
• Reduced Anchor Approaches: EfficientDet and YOLOv4 optimize the number of anchor boxes used, balancing detection speed and accuracy.

These approaches aim to improve object detection efficiency while maintaining high performance, particularly for real-time applications.

In summary, anchor boxes are a crucial part of object detection, helping models detect objects efficiently across different sizes and aspect ratios. However, new advancements are exploring ways to reduce or eliminate anchor boxes for even faster and more flexible detection.