【推荐】YOLO实时目标检测(6fps)

2017 年 11 月 5 日 机器学习研究会
【推荐】YOLO实时目标检测(6fps)


点击上方 “机器学习研究会”可以订阅


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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Yolo算法,其全称是You Only Look Once: Unified, Real-Time Object Detection,You Only Look Once说的是只需要一次CNN运算,Unified指的是这是一个统一的框架,提供end-to-end的预测,而Real-Time体现是Yolo算法速度快。

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/facebookresearch/Detectron

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Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection performance compared to other approaches, with YOLOv2 (an improved You Only Look Once model) being one of the state-of-the-art in DNN-based object detection methods in terms of both speed and accuracy. Although YOLOv2 can achieve real-time performance on a powerful GPU, it still remains very challenging for leveraging this approach for real-time object detection in video on embedded computing devices with limited computational power and limited memory. In this paper, we propose a new framework called Fast YOLO, a fast You Only Look Once framework which accelerates YOLOv2 to be able to perform object detection in video on embedded devices in a real-time manner. First, we leverage the evolutionary deep intelligence framework to evolve the YOLOv2 network architecture and produce an optimized architecture (referred to as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To further reduce power consumption on embedded devices while maintaining performance, a motion-adaptive inference method is introduced into the proposed Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2 based on temporal motion characteristics. Experimental results show that the proposed Fast YOLO framework can reduce the number of deep inferences by an average of 38.13%, and an average speedup of ~3.3X for objection detection in video compared to the original YOLOv2, leading Fast YOLO to run an average of ~18FPS on a Nvidia Jetson TX1 embedded system.

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