
Object detection: an overview in the age of Deep Learning - mfagio
https://tryolabs.com/blog/2017/08/30/object-detection-an-overview-in-the-age-of-deep-learning/
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pavlov
The progress in this field is very exciting. But the last section of the fine
article makes a really important point -- ultimately the training dataset
defines the output that the algorithms can provide:

 _" Unfortunately, there aren’t enough datasets for object detection. Data is
harder (and more expensive) to generate, companies probably don’t feel like
freely giving away their investment, and universities do not have that many
resources."_

Even ImageNet has only 200 classes. Imagine a person with a vocabulary of
exactly 200 concepts trying to describe the world. As Wittgenstein wrote, "The
limits of my language mean the limits of my world" \-- and the language we can
teach to computers is still very limited.

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melling
Is this something that can be crowdsourced?

Mozilla is crowd sourcing voice data, for example:
[https://voice.mozilla.org](https://voice.mozilla.org)

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alexbeloi
Yes, but somebody still has to pay or donate their time and the 'human'
computational time require is pretty heavy for an object detection task.

It's also usually ideal to just have one person label one image, people often
miss things or mislabel things. If you want low noise data, you need to have
people repeatedly labeling the same image until they reach a consensus. The
more objects the more opportunity for confusion, requiring more labeling.

You have probably been prompted to use captcha-like thing for selecting
portions of an image containing a street sign. That's object detection, for a
single class of object.

To answer your question, yes it should be crowdsourced, yes it is being
crowdsourced, but the companies that are doing it are keeping the data to
themselves because of the value/expense in collecting it.

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vikiomega9
From a company's perspective this is essentially their business strategy and
perhaps it makes sense to crowdfund crowdsourcing or something along the likes
of SETI@Home.

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prodtorok
How far out are we from Object Detection that can identify at different
scales? - i.e. angled, facing towards, rotated, skewed, etc...

We seem to have good OD that can create horizontal bounding boxes, but these
bounding boxes seem to be generic estimates.

Even rectangle detection with these models can't identify the angle or skew of
a rectangle in a frame (we get the same generic bounding box)

OpenCV seemed to have models awhile ago that could do this just fine.

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alexcnwy
You can re-pose it as a regression problem to detect the coordinates of the 4
corners in the case of a single object in the image.

