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.

Updated on: 14-Oct-2022


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