【推荐】YOLO实时目标检测(6fps)

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摘要

 

转自：爱可可-爱生活

Object detection is one of the classical problems in computer vision:

Recognize what the objects are inside a given image and also where they are in the image.

Detection is a more complex problem than classification, which can also recognize objects but doesn’t tell you exactly where the object is located in the image — and it won’t work for images that contain more than one object.

YOLO is a clever neural network for doing object detection in real-time.

In this blog post I’ll describe what it took to get the “tiny” version of YOLOv2 running on iOS using Metal Performance Shaders.

Before you continue, make sure to watch the awesome YOLOv2 trailer. 😎

How YOLO works

You can take a classifier like VGGNet or Inception and turn it into an object detector by sliding a small window across the image. At each step you run the classifier to get a prediction of what sort of object is inside the current window. Using a sliding window gives several hundred or thousand predictions for that image, but you only keep the ones the classifier is the most certain about.

This approach works but it’s obviously going to be very slow, since you need to run the classifier many times. A slightly more efficient approach is to first predict which parts of the image contain interesting information — so-called region proposals — and then run the classifier only on these regions. The classifier has to do less work than with the sliding windows but still gets run many times over.

YOLO takes a completely different approach. It’s not a traditional classifier that is repurposed to be an object detector. YOLO actually looks at the image just once (hence its name: You Only Look Once) but in a clever way.

链接：

http://machinethink.net/blog/object-detection-with-yolo/

原文链接：

https://m.weibo.cn/1402400261/4170632415278041

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