lanenet-lane-detection

Why we included this project

Teams prototyping lane detection for autonomous driving or driver assistance will find LaneNet a practical starting point, and this unofficial TensorFlow implementation is a solid one to study. It pairs an encoder-decoder backbone with two heads: one that produces binary lane masks and another that runs instance segmentation with a discriminative loss, so each lane line comes back as a separate instance instead of one merged blob. Pretrained weights trained on the TuSimple benchmark are included, and you can run inference on a single image right away, with the GPU portion of the pipeline hitting roughly 50 fps. The code is also a useful learning resource, since it walks through preparing TuSimple-style training records, generating binary and instance label maps, and training with VGG or custom encoder backbones. Just be aware the setup targets an older TensorFlow stack, so treat it as a proven baseline to adapt rather than a drop-in production package.