I cannot wait to see what you two are putting together. Thanks from the community for your continued work and research.
The visualization is @DamianXVI's work. Capability-wise it could be related to v9's "360 object decoder" that @jimmy_d talks about in his fantastic post over at the Neural Networks thread
WOW super cool! Could you give a quick example of what the data for these boxes looks like? Is it like box size + polar coordinates for box centroid + box orientation info?
How German of it. I finally get to try it out so I'm curious to see how my choices compare with its. I don't get to try the ULC yet, but I'll try to be a good obedient driver in changing lanes when it wants. It does need to be regional because people drive differently in different regions. I fully expect it to be in the right lane more than me on a three lane road. I move over since I feel the middle lane is safer, and I only use the passing lane when I need to pass.
@verygreen apart from the orange ribbon. I wonder if those lane lines are given in 3D space coordinate too?
@verygreen what's the output of aknet lane? coordinates on image space? or mathematical lane function?
what do you mean by the aknat lane? the lane lines are just a few points bezier curves in image space.
aknet lane means lane detection decoders in v9 neural network. so the network outputs bezier curve control points in image space. that's exactly what i've wondered. thank you very much!