# MaybeShewill-CV/lanenet-lane-detection

Unofficial implemention of lanenet model for real time lane detection

Repository: https://github.com/MaybeShewill-CV/lanenet-lane-detection
Canonical: https://ross.abutalabs.com/products/lanenet-lane-detection
Language: Python
License: Apache-2.0
License Family: permissive
Topics: lanenet, self-driving-car, deep-learning, instance-segmentation, tensorflow, lane-detection, lane-finding, lane-lines-detection
Last push: 2023-12-08T16:17:26+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3019, "days_push": 999, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2562, forks 896 (observed 2026-08-28T04:07:00.995636+00:00)

## What it is
An unofficial TensorFlow implementation of the LaneNet deep neural network for real-time lane detection, based on the IEEE IV paper 'Towards End-to-End Lane Detection: an Instance Segmentation Approach'. It combines binary semantic segmentation and instance segmentation with a discriminative loss to detect lane lines, with pretrained weights on the TuSimple dataset.

## Use cases
- detect lane lines in driving images
- run real-time lane detection for autonomous driving research
- segment lanes using instance segmentation
- evaluate a lane detection model on the TuSimple dataset
- retrain or fine-tune LaneNet on custom lane datasets

## When to choose
- you need a ready-to-use LaneNet implementation with pretrained TuSimple weights
- you are doing research or prototyping on lane detection with TensorFlow 1.x
- you want to reproduce results from the LaneNet instance segmentation paper

## When to avoid
- you need PyTorch or TensorFlow 2.x support
- you need a production-grade, actively maintained lane detection pipeline
- you require real-time video input processing out of the box, since the input pipeline is not fully optimized

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, deep-learning, machine-learning
- domain: computer-vision, autonomous-vehicles, deep-learning, image-processing
- platform: python
- tags: lane-detection, instance-segmentation, semantic-segmentation, tensorflow, self-driving, tusimple-dataset, encoder-decoder, linux, gpu

## Member repositories
- MaybeShewill-CV/lanenet-lane-detection (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:00.995636+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:23:29.622935+00:00, confidence not recorded.
  - readme: https://github.com/MaybeShewill-CV/lanenet-lane-detection (fetched 2026-08-28T04:07:00.995636+00:00, sha 2dd1478eb5e0)
- Data as of 2026-08-30T08:39:29.467469+00:00.