The single-shot multibox detection (SSD) model is a multiscale object detection framework that operates in a single forward pass. It primarily consists of a base network followed by a sequence of multiscale feature map blocks. The architecture generates varying numbers of anchor boxes at different sizes across these blocks, and detects varying-size objects by simultaneously predicting the classes and spatial offsets of these anchor boxes.

Claude

University of Michigan - Ann Arbor

There are a few different algorithms for object detection and they can be split into two groups:

1. Algorithms based on classification. They are implemented in two stages. First, they select regions of interest in an image. Second, they classify these regions using convolutional neural networks. This solution can be slow because we have to run predictions for every selected region. A widely known example of this type of algorithm is the Region-based convolutional neural network (RCNN) and its cousins Fast-RCNN, Faster-RCNN and the latest addition to the family: Mask-RCNN. Another example is RetinaNet.

2. Algorithms based on regression – instead of selecting interesting parts of an image, they predict classes and bounding boxes for the whole image in one run of the algorithm. The two best known examples from this group are the YOLO (You Only Look Once) family algorithms and SSD (Single Shot Multibox Detector). They are commonly used for  real-time object detection as, in general, they trade a bit of accuracy for large improvements in speed.

Two Main Groups of Object Detection Algorithms

Feature maps extracted from different layers of a convolutional neural network possess varying-size receptive fields with respect to the input image. This hierarchical structure can be leveraged for multiscale object detection by designing a network where feature maps closer to the output layer, which have wider receptive fields, are responsible for detecting larger objects, while shallower feature maps detect smaller objects.

Multiscale Object Detection via Receptive Fields

Dive into Deep Learning

This algorithm uses a single convolution neural network but instead of processing the image pixel by pixel and developing more broad features with every step in the hidden layer. It takes in the whole image and performs the object detection in a single regression problem. It first splits up the image into an SxS grid before passing it into the neural network where bounding boxes and class predictions are then created. Intersect over Union and non-maximum suppression are needed to formulate the founded boxes.

YOLO (You Only Look Once)

Single-Shot Multibox Detection Model

The Region-based Convolutional Neural Network (R-CNN) is an object detection model that operates by first extracting a large number of region proposals (e.g., $$2000$$) from an input image. Anchor boxes can also be considered as such proposals. A Convolutional Neural Network (CNN) is then used to perform forward propagation on each individual region proposal to extract its features. Finally, these features are utilized to predict the class and bounding box for each proposed region.

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