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.

Most neural networks are structured around a classification task with pre-tagged data. This is fundamentally a problem that it requires pre-tagged data in order to ferret out what the "true meaning" of a particular classification is.

When the task moves towards determining differences between un-tagged data, you're looking at more of a clustering exercise. This is a murkier machine learning task which is relatively underdeveloped. As a quick comparison, linear regression and correlations were generally worked out by Pearson back in the late 1800's, while k-means clustering was only published openly since the 1980's.

Looks like great project! It is a pity they don't provide the data for download yet.

The Tatoeba project focuses on crowdsourcing translations of sentences but has also around a few thousand spoken ones in a variety of languages. Everything is available for download for free.

https://tatoeba.org/eng