https://github.com/asad-ismail/lane_detection

Lane Detection and Classification using Front camera monocular images
https://github.com/asad-ismail/lane_detection

adas cpp embedded lanedetection pruning quantization tensorflow weight weightclustering

Last synced: 12 months ago
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Lane Detection and Classification using Front camera monocular images

• Host: GitHub
• URL: https://github.com/asad-ismail/lane_detection
• Owner: Asad-Ismail
• Created: 2021-02-23T15:44:06.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2023-03-02T17:00:31.000Z (about 3 years ago)
• Last Synced: 2025-03-29T08:43:42.818Z (about 1 year ago)
• Topics: adas, cpp, embedded, lanedetection, pruning, quantization, tensorflow, weight, weightclustering
• Language: Python
• Homepage:
• Size: 40.9 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🚗 Ego and Side Lane Detection for ADAS Applications (Tensorflow 2.x) 🚘

## [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/Asad-Ismail/lane_detection/issues) [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FAsad-Ismail%2Flane_detection&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)

## 🌟 Features

1. Segement and classify ego, left and right lane lines. The output can be visualized as shown below:

  


    

  

  

2. Apply pruning, clustering, and quantization to miniaturize the model, making it embedded system-ready.


3. C++ inference to use the resulting miniature model.

## 📚 Training Dataset:

* The training data is TU Simple Lane Detection dataset.You can access it through this link: https://github.com/TuSimple/tusimple-benchmark

* Dataset is preprocessed to annotate the lanes in 4 categories left ego (label=2), right ego (label=1), right lane (label=3), and left lane line (label=4)

* Images are 1280x720 RGB, and labels are 1280x720 grayscale images

* Image augmentations like rotation, flipping, saturation, brightness, and contrast changes are applied randomly

* Data pipeline is made efficient using data interleaving and prefetch

## 🧰 Model:

* MobileNetV2 is used as the backbone network, and then transposed convolutions are applied for upsampling with UNET-like feature concatenation.

* ResNets or EfficientNets can also be used as a backbone for better performance.

## 💻 Training and Prediction

* Install requirements using pip install -r requirements.txt

* Run training using python train.py --train_images [path to train images] --train_labels [path to train labels]

* Perform prediction on image or video using pred.py or pred_video.py (also writes the blended video).

* Prune, cluster, and quantize model weights and activations for miniaturization.

  


    

  

Applying the above pipeline reduces the model size by approximately 11x, from 24MB to 2.2MB.

  


    

  

  

* Pretrained weights are available at https://drive.google.com/drive/folders/1EhQ-8UoFv4rvMqe2mrJ4HFzZATd_Ee8c?usp=sharing.