[chintler]

You can pretty much solve this using modern DL models. There are options depending on how accurate you want your model and how much compute you have.

There is an entire spectrum of models, from something like Mask-RCNN, U-Net family upto something like Meta's SAM, which you can use without even training.

[eurekin]

Thanks! Would they also help with reading the exact rpm and speed values?

[sgu999]

In this kind of cases I'd probably use the Hough-based algo as ground truth to see if you can indeed fine-tune a DNN on that regression task. If it does with reasonable accuracy, then you have a baseline that could be improved in multiple ways to surpass the original.

That said, there are not that many shapes of speedometer and wheels, and the view point is likely controlled, so your old school method is probably the better way ;)

[eurekin]

For the purpose of learning, would you recommend some tutorials, articles or videos that help achieve that? Accuracy aside, this would make a great learning experience!

Is it better to look in the PyTorch community, or that's where some Tensorflow approaches shine? (CUDA is ok)

[sgu999]

PyTorch is much nicer to play with in my opinion. Maybe start with their official tutorial, I've also heard good things about Karpathy's YouTube channel from beginners.