So now medium is straight up not letting people read an article unless they're logged in ... Disgusting.
I built a DIY license plate reader with a Raspberry Pi and machine learning
11–20 of 78 posts
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#12The obvious question is why not use a local accelerator?
Either the Neural Compute Stick or the Google Coral both have more than enough grunt to run real-time object detection models. Both will run on USB2 power. I don't know the overhead of good OCR, but license plates are a very standard format so perhaps you could train a second detector to extract the letters?
Even if you do OCR in the cloud, local bounding box extraction would save a huge amount of bandwidth.
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#13"Machine Learning" is the wrong tool for the job here.
Tesseract OCR can do this, using only the Raspberry Pi, at a "good enough" framerate for any real driving situation.
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#14The obvious question is why not use a local accelerator? Either the Neural Compute Stick or the Google Coral both have more than enough grunt to run real-time object detection models. Both will run on USB2 power. I don't know the overhead of good OCR, but license plates are a very standard format so perhaps you could train a second detector to extract the letters? Even if you do OCR in the cloud, local bounding box e…
Hey, founder of a relevant startup here. Just wanna chime in on OCR + bounding boxes performance. We offer text recognition with bounding boxes as a service. Our average processing duration, between reading bytes off the wire and writing the JSON response, is just under 3 seconds on average. Obviously that throws it out the window for frame-by-frame applications, but I think it’s still worth mentioning. The recognition is just as accurate as Google Cloud Vision —- it can handle human handwriting and even cursive, in most cases.
If you’re interested in trying it out: https://siftrics.com/
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#15The obvious question is why not use a local accelerator? Either the Neural Compute Stick or the Google Coral both have more than enough grunt to run real-time object detection models. Both will run on USB2 power. I don't know the overhead of good OCR, but license plates are a very standard format so perhaps you could train a second detector to extract the letters? Even if you do OCR in the cloud, local bounding box e…
Probably yeah, but the potential of the cloud was much more appealing to me.
Detecting the license plates is really cheap computationally speaking, but not on the RPi. The most expensive part computationally was identifying the words (letters) - that's because detecting the text within the bounding boxes obtained from YOLOv3 is based on a VGG-16 model. Running that multiple times in a single frame (for multiple license plates) is expensive.
Surprisingly, the bandwidth was the least of my concerns. I was very surprised to see I didn't need much at all. For YOLOv3@ 416p and @30FPS I need about ~3Mbps. I wouldn't consider that much.
Now, this is a demo of what a production system could theoretically look like. I know it could be much better optimized.
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#16It’s just a question of time before people can hack their own similar facial recognition system. The new glasses from Bosch, with a projector straight on to the retina, is an obvious choice for displaying personal info about everybody in reach of the camera. With 5G, everybody has a reliable access to all the computing power needed.
Link ?
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#17So now medium is straight up not letting people read an article unless they're logged in ... Disgusting.
There is also a limit on articles you can read before they throw up a paywall. No clue why anybody would use this as their blogging platform.
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#18The obvious question is why not use a local accelerator? Either the Neural Compute Stick or the Google Coral both have more than enough grunt to run real-time object detection models. Both will run on USB2 power. I don't know the overhead of good OCR, but license plates are a very standard format so perhaps you could train a second detector to extract the letters? Even if you do OCR in the cloud, local bounding box e…
I'd think a Jetson Nano would be more than enough as well. No need to invoke the cloud.
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#19So now medium is straight up not letting people read an article unless they're logged in ... Disgusting.
Disgusting, indeed. But as long as these links are accepted here, nothing won't change.
Take this in the meantime: https://outline.com/qsv7ab
Re: I built a DIY license plate reader with a Raspberry Pi and machine learning
#20"Machine Learning" is the wrong tool for the job here. Tesseract OCR can do this, using only the Raspberry Pi, at a "good enough" framerate for any real driving situation.
It's more like a "learnèd machine" approach. No need to train the model, but a pre-trained model may be (or not be, depending on circumstances!) more efficient than "hand-written" OCR approaches.