Is it difficult to learn the rotated bounding box for an object?

Object detection is a computer vision technique that identifies and localizes objects in an image or video. In object detection algorithms, the bounding boxes (BB) specify the area of interest or label the data within an image when training a detection algorithm/model. It is also used to predict the model output.

There are different kinds of bounding boxes used in object detection. We will compare two of them—rotated and axis-aligned—below:

Axis-aligned bounded box

• Shape: It is a rectangular-shaped bounding box; as the name suggests, it is always horizontally or vertically aligned.

• Orientation: The orientation of an axis-aligned bounded box is not altered by the orientation of an object.

• Parameters: It is defined using four parameters: top-left values (x, y), width, and height.

• Applications: Axis-aligned bounded boxes are commonly used in object detection tasks because they provide an efficient and simple way to enclose objects within rectangles aligned with the axes. They are used in many computer vision applications, such as vehicle and face detection. They are widely used in games and simulations for collision detection due to their simplicity.

Rotated bounding box

• Shape: It can be a rectangle, square, or parallelogram-shaped bounding box. A rotated bounding box is used in computer vision to tightly enclose an object or region of interest within an image. The rotated bounding box is more versatile for capturing irregularly shaped objects.

• Orientation: The orientation of a rotated bounded box is dependent upon the orientation or rotation angle of objects.

• Parameters: It is a bit complex and defined using five parameters: center values (x, y), width, height, and angle.

• Applications: Rotated bounded boxes are used when axis-aligned bounding boxes cannot represent objects accurately due to their orientation. They are commonly used to detect rotated license plates of vehicles or objects captured by satellites. They check for collisions with other objects in a rotated orientation, which is common in robotics and physics simulations.

The difficulty of learning a rotated bounding box for an object depends on different factors such as model selection, data quality, data quantity, etc. There is a trade-off between accuracy and complexity. However, the rotated bounded box is a bit difficult and complex to learn compared to the axis-aligned bounding box. Nonetheless, both have various applications in object detection, text detection, collision detection, robotics, and medical imaging.