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Difference between YOLO and SSD
YOLO and SSD are real-time object detection systems that possess significant differences, that have been listed below −
YOLO (You Only Look Once)
YOLO uses a neural network to help with real-time object detection. It became popular due to its speed and accuracy.
It is considered a regression problem, where the algorithm looks at the object/s only once. There are algorithms associated with YOLO that achieve 155 FPS (frames per second). Image is divided into a grid, and every grid calculates class probabilities and bounding box parameters to determine the object in its entirety.
It is an open-source detection technique that works with images and videos.
It is preferred when the object size is small as well.
It can be used with self-driving cars, and other salient applications of artificial intelligence.
SSD (Single Shot Detector)
SSD works well with real-time object detection. It discretizes the output space of the bounding boxes into a couple of default boxes.
These default boxes are of different ratios and scales per feature map location.
The network generates a score for the presence of every object category in every default box and produces adjustments to the box.
These adjusts are made to match the shape of the object.
The network combines the predictions from different feature maps with different resolutions thereby handling objects of varying sizes gracefully.
The speed is a result of eliminating the bounding box proposals and feature resampling.
This includes a convolutional filter that predicts object categories and offsets in the bounding box locations using filters (separate predictors) for different sized objects.
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