Introduction to One-Stage Object Detection Architectures
Explore one-stage object detection architectures used in real-time image and video analysis. Understand how models like YOLO and SSD generate region proposals and classify objects simultaneously to achieve faster detection without sacrificing accuracy. This lesson covers the evolution of key one-stage detectors and their optimizations to help you choose suitable models for different applications.
We'll cover the following...
Detecting objects at one step
Extracting the regions from an image using the feature maps, then predicting the probability of these regions, whether they have an object inside or not, and finally sending the chosen high-probability regions to the classifier head seems to work quite accurately but slowly on the other hand.
Depending on the project we work on, we might need different expectations from our model regarding its speed. If we work on live videos, we will need a model able to process the frames at 30 FPS (the most common video setting, but it might be more or less than that) to catch the next frame coming from our live stream.
We might need more time to process a video that is not live but offline. Anyway, it wouldn’t be the best option to process a one-minute video in 10 minutes.
In ...