Update on 2020/07/20. Provide model trained on my own datasets.

• Support different backbones
• Multi-GPU training

A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing https://www.mdpi.com/1424-8220/20/16/4544

This is a PyTorch(1.4.0+cu100) implementation of Laser-stripe-detection-neural-network(LSDNN) which can eliminate the interference of reflective noise and haze noise and realize the highly robust extraction of laser stripes region.

The result of segmentation and extraction dealing with different noises. the input image (1920×1920 pixels). The middle column is the output of our network, where different colors denote different categories. The right column is the result of the center extraction based on the segmentation. The laser stripe’s center is marked as green.

The result of 3D reconstruction

Dataset we used is made by ourselves. You can download the dataset from [baiduyun](链接:https://pan.baidu.com/s/1FA93B6Gzby5eESHk-RnT_Q 密码:gq6u).

You can download the more result image from [baiduyun](链接:https://pan.baidu.com/s/1TWJXz_bkGmUVgUi4qZUCIA 密码:61uo).

The code was tested with Anaconda and Python 3.6. After installing the Anaconda environment:

0. Clone the repo:

   git clone [email protected]:zhaocongyang/LSDNN.git
   cd LSDNN

1. Install dependencies:

   For PyTorch dependency, see pytorch.org for more details.

   For custom dependencies:

   pip install matplotlib pillow tensorboardX tqdm

Fellow steps below to train your model:

0. Configure your dataset path in mypath.py.

1. To train LSDNN using our dataset and ResNet as backbone:

   sh train_voc.sh

Fellow steps below to test your model:

0. You can download model trained by us from [baiduyun](链接:https://pan.baidu.com/s/1h4X0UaKIeff4ZBC1oU1C2A 密码:r3k7).

1. To test the model

   CUDA_VISIBLE_DEVICES=0 python test_center_gpu_sensors.py

Fellow steps below to run a demo:

0. Configure your image path

1. To run a demo

   CUDA_VISIBLE_DEVICES=0 python single_demo.py