Anchor boxes, alternatively referred to as prior boxes, are pre-established bounding boxes that play a crucial role in object detection algorithms. These boxes serve as a means to assist in the identification of objects within an image. They are usually selected based on the characteristic sizes and aspect ratios of the objects that the algorithm aims to detect. For instance, in the scenario where the algorithm is designed to detect cars, anchor boxes with a broad, rectangular aspect ratio are likely to be chosen, as they closely match the typical shape of a car.
When an object detection algorithm processes an image, it systematically applies a set of anchor boxes to various locations throughout the image. For each individual anchor box, the algorithm employs a backbone network, which can be either a Convolutional Neural Network (CNN) or a Transformer, to determine whether the box encompasses an object or not. In the case where an object is present, the network is further utilized to classify which specific class the object belongs to. Additionally, the algorithm relies on the network to accurately predict the coordinates of the bounding box that encapsulates the object.
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing grid-like data, such as images. They use convolutional layers, which apply a set of learnable filters to the input data. These filters slide over the input image, performing convolution operations at each position. This process helps in extracting local features, such as edges, corners, and textures. Pooling layers are often used in CNNs to downsample the feature maps, reducing the computational complexity while retaining the most important information. The fully-connected layers at the end of the network are used for classification and regression tasks.
Transformer
Transformers, on the other hand, are a relatively new architecture in the field of deep learning. They were initially introduced for natural language processing tasks but have since been adapted for computer vision, including object detection. Transformers are based on the self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence. In the context of object detection, a Transformer-based model can process the entire image as a sequence of patches. This architecture enables the model to capture long-range dependencies between different parts of the image, which can be beneficial for detecting objects in complex scenes.
Currently, some of the cutting-edge object detection models based on the Transformer architecture include T-Rex2, Grounding DINO, DINO-X, and so on.