Fuse radar and camera for detection

Related tags

Deep LearningSAF-FCOS
Overview

SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor

This project hosts the code for implementing the SAF-FCOS algorithm for object detection, as presented in our paper:

SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor;
Shuo Chang, YiFan Zhang, Fan Zhang, Xiaotong Zhao, Sai Huang, ZhiYong Feng and Zhiqing Wei;
In: Sensors, 2019.

And the whole project is built upon FCOS, Below is FCOS license.

FCOS for non-commercial purposes

Copyright (c) 2019 the authors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

The full paper is available at: https://www.mdpi.com/1424-8220/20/4/956.

You should known

Please read the FCOS project first FCOS-README.md

Installation

Please check INSTALL.md for installation instructions.

Generate Data

  1. Please download Full dataset (v1.0) of nuScenes dataset from the link. download

  2. Then, upload all download tar files to an ubuntu server, and uncompress all *.tar files in a specific folder:

mkdir ~/Data/nuScenes
mv AllDownloadTarFiles ~/Data/nuScenes
cd ~/Data/nuScenes
for f in *.tar; do tar -xvf "$f"; done
  1. Convert the radar pcd file as image:
python tools/nuscenes/convert_radar_point_to_image.py --dataroot ~/Data/nuScenes --version v1.0-mini
python tools/nuscenes/convert_radar_point_to_image.py --dataroot ~/Data/nuScenes --version v1.0-trainval
python tools/nuscenes/convert_radar_point_to_image.py --dataroot ~/Data/nuScenes --version v1.0-test
  1. Calculate the norm info of radar images:
python tools/nuscenes/extract_pc_image_norm_info_from_image.py --datadir ~/Data/nuScenes --outdir ~/Data/nuScenes/v1.0-trainval
  1. Generate 2D detections results for nuScenes CAM_FRONT images by 'FCOS_imprv_dcnv2_X_101_64x4d_FPN_2x.pth',
    some of detection results should be refined by labelers to get tighter bboxes,
    and save the detection results as txt file in the folder ~/Data/nuScenes/fcos/CAM_FRONT:
    detection1 detection2 The detection results are saved as '0, 1479.519, 611.043, 1598.754, 849.447'. The first column is category, and the last stands for position.
    For convenience, we supply our generated 2D txt files in cloud drive and in folder data/fcos.zip.
    For users not in China, please download from google drive.
    For users in China, please download from baidu drive.

    链接:https://pan.baidu.com/s/11NNYpmBbs5sSqSsFxl-z7Q 
    提取码:6f1x 

    If you use our generated txt files, please:

mv fcos.zip ~/Data/nuScenes
unzip fcos.zip
  1. Generate 2D annotations in coco style for model training and test:
python tools/nuscenes/generate_2d_annotations_by_fcos.py --datadir ~/Data/nuScenes --outdir ~/Data/nuScenes/v1.0-trainval

Prepare training

The following command line will train fcos_imprv_R_101_FPN_1x_ATTMIX_135_Circle_07.yaml on 8 GPUs with Synchronous Stochastic Gradient Descent (SGD):

python -m torch.distributed.launch \
       --nproc_per_node=8 \
       --master_port=$((RANDOM + 10000)) \
       tools/train_net.py \
       --config-file configs/fcos_nuscenes/fcos_imprv_R_101_FPN_1x_ATTMIX_135_Circle_07.yaml \
       DATALOADER.NUM_WORKERS 2 \
       OUTPUT_DIR tmp/fcos_imprv_R_50_FPN_1x

Prepare Test

The following command line will test fcos_imprv_R_101_FPN_1x_ATTMIX_135_Circle_07.yaml on 8 GPUs:

python -m torch.distributed.launch \
       --nproc_per_node=8  
       --master_port=$((RANDOM + 10000)) \
       tools/test_epoch.py \
       --config-file configs/fcos_nuscenes/fcos_imprv_R_101_FPN_1x_ATTMIX_135_Circle_07.yaml \
       --checkpoint-file tmp/fcos_imprv_R_50_FPN_1x_ATTMIX_135_Circle_07/model_0010000.pth \ 
       OUTPUT_DIR tmp/fcos_imprv_R_101_FPN_1x_ATTMIX_135_Circle_07

Citations

Please consider citing our paper and FOCS in your publications if the project helps your research. BibTeX reference is as follows.

@article{chang2020spatial,
  title={Spatial Attention fusion for obstacle detection using mmwave radar and vision sensor},
  author={Chang, Shuo and Zhang, Yifan and Zhang, Fan and Zhao, Xiaotong and Huang, Sai and Feng, Zhiyong and Wei, Zhiqing},
  journal={Sensors},
  volume={20},
  number={4},
  pages={956},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}
@inproceedings{tian2019fcos,
  title   =  {{FCOS}: Fully Convolutional One-Stage Object Detection},
  author  =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  booktitle =  {Proc. Int. Conf. Computer Vision (ICCV)},
  year    =  {2019}
}
Owner
ChangShuo
Machine learning. Visual Object Tracking. Signal Processing. Multi-Sensor Fusion
ChangShuo
Official pytorch implementation of DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces

DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces Minhyuk Sung*, Zhenyu Jiang*, Panos Achlioptas, Niloy J. Mitra, Leonidas

Zhenyu Jiang 21 Aug 30, 2022
The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

Ren Yurui 111 Dec 10, 2022
IGCN : Image-to-graph convolutional network

IGCN : Image-to-graph convolutional network IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstructi

Megumi Nakao 7 Oct 27, 2022
Predictive AI layer for existing databases.

MindsDB is an open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning

MindsDB Inc 12.2k Jan 03, 2023
📚 Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. Papermill lets you: parameterize notebooks execute notebooks This

nteract 5.1k Jan 03, 2023
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine

FlyEgle 214 Dec 29, 2022
GeoTransformer - Geometric Transformer for Fast and Robust Point Cloud Registration

Geometric Transformer for Fast and Robust Point Cloud Registration PyTorch imple

Zheng Qin 220 Jan 05, 2023
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

SalGAN: Visual Saliency Prediction with Adversarial Networks Junting Pan Cristian Canton Ferrer Kevin McGuinness Noel O'Connor Jordi Torres Elisa Sayr

Image Processing Group - BarcelonaTECH - UPC 347 Nov 22, 2022
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

730 Jan 09, 2023
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

19 Dec 10, 2022
Asymmetric metric learning for knowledge transfer

Asymmetric metric learning This is the official code that enables the reproduction of the results from our paper: Asymmetric metric learning for knowl

20 Dec 06, 2022
Face recognize system

FRS Face_recognize_system This project contains my work that target on solving some problems of FRS: Face detection: Retinaface Face anti-spoofing: Fo

Tran Anh Tuan 4 Nov 18, 2021
This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation.

ERFNet This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation. NEW!! New PyTorch

Edu 104 Jan 05, 2023
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve

Google Research 29 Nov 03, 2022
The Ludii general game system, developed as part of the ERC-funded Digital Ludeme Project.

The Ludii General Game System Ludii is a general game system being developed as part of the ERC-funded Digital Ludeme Project (DLP). This repository h

Digital Ludeme Project 50 Jan 04, 2023
Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Demetri Pananos 9 Oct 04, 2022
给yolov5加个gui界面,使用pyqt5,yolov5是5.0版本

博文地址 https://xugaoxiang.com/2021/06/30/yolov5-pyqt5 代码执行 项目中使用YOLOv5的v5.0版本,界面文件是project.ui pip install -r requirements.txt python main.py 图片检测 视频检测

Xu GaoXiang 215 Dec 30, 2022
A concise but complete implementation of CLIP with various experimental improvements from recent papers

x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install $ pip install x-clip Usag

Phil Wang 515 Dec 26, 2022
Simulation code and tutorial for BBHnet training data

Simulation Dataset for BBHnet NOTE: OLD README, UPDATE IN PROGRESS We generate simulation dataset to train BBHnet, our deep learning framework for det

0 May 31, 2022