LaneDetection: ENet-SAD - Implement/Test lane detection (open source dataset)

[Christiaan1990]

Goal:

Implement, train and test a ENet-SAD (Self Attention Distillation) algorithm and test it with the open source datasets CULane and TuSimple

Assigments:

• Understand how the ML-algorithms works
• Implement the ML-algorithms
• Train and test it with an open source dataset
• Documentation in the TUM-Wiki: https://wiki.tum.de/display/phoenix/Machine+Learning

Notes:

• https://paperswithcode.com/task/lane-detection/codeless
• paper: https://arxiv.org/abs/1908.00821
• optional dataset: https://xingangpan.github.io/projects/CULane.html
• optional dataset: tusimple benchmark ground truth TuSimple/tusimple-benchmark#3
• basic information about lane detection: https://wiki.tum.de/display/phoenix/Lane+Detection