cardwing/Codes-for-Lane-Detection

Health v2 · maintenance only

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1075 stars · 333 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementations of lightweight lane detection CNNs, including the ENet-SAD model from the ICCV 2019 paper 'Learning Lightweight Lane Detection CNNs by Self Attention Distillation' plus a TensorFlow reimplementation of SCNN. Models are provided in Torch/Lua and PyTorch with training and testing code for the TuSimple, CULane, and BDD100K datasets.

Use cases

• detect lane markings in dashcam or driving footage
• train a lightweight lane detection model on CULane
• reproduce self attention distillation results from the ICCV 2019 paper
• benchmark lane detection on TuSimple and BDD100K
• run SCNN in TensorFlow for traffic scene understanding
• deploy a fast low-parameter lane segmentation network for autonomous driving research

When to choose

• you need a lightweight, fast lane detection model with published benchmarks
• you want to reproduce or build on the SAD or SCNN papers
• you are doing autonomous driving perception research on TuSimple, CULane, or BDD100K

When to avoid

• you need a production-ready, actively maintained lane detection pipeline
• you want a plug-and-play detector without touching research code
• you need support for modern framework versions (code targets TensorFlow 1.x and older PyTorch)
• your task is general semantic segmentation rather than lane detection

Facets

library · maturity maintenance

deep-learning computer-vision machine-learning image-processing computer-vision autonomous-vehicles deep-learning machine-learning python lane-detection self-attention-distillation knowledge-distillation semantic-segmentation cnn pytorch tensorflow torch autonomous-driving research-code enet scnn erfnet tusimple culane bdd100k linux gpu

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