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.

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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

YOLO (You Only Look Once)

Similar to YOLO, the SSD only uses a single forward pass for object detection and localization. What makes it different is it uses a multibox technique for determining bounding boxes which is a fast class-agnostic bounding box coordinate proposal.

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