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
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021

Learning Generative Models of Textured 3D Meshes from Real-World Images This is the reference implementation of "Learning Generative Models of Texture

Dario Pavllo 115 Jan 07, 2023
StyleTransfer - Open source style transfer project, based on VGG19

StyleTransfer - Open source style transfer project, based on VGG19

Patrick martins de lima 9 Dec 13, 2021
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation (ICCV2021)

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad

DV Lab 29 Nov 21, 2022
DeLighT: Very Deep and Light-Weight Transformers

DeLighT: Very Deep and Light-weight Transformers This repository contains the source code of our work on building efficient sequence models: DeFINE (I

Sachin Mehta 440 Dec 18, 2022
Realtime YOLO Monster Detection With Non Maximum Supression

Realtime-YOLO-Monster-Detection-With-Non-Maximum-Supression Table of Contents In

5 Oct 07, 2022
Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

TFlite Ultra Fast Lane Detection Inference Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite. So

Ibai Gorordo 12 Aug 27, 2022
Implementation of paper "DeepTag: A General Framework for Fiducial Marker Design and Detection"

Implementation of paper DeepTag: A General Framework for Fiducial Marker Design and Detection. Project page: https://herohuyongtao.github.io/research/

Yongtao Hu 46 Dec 12, 2022
salabim - discrete event simulation in Python

Object oriented discrete event simulation and animation in Python. Includes process control features, resources, queues, monitors. statistical distrib

181 Dec 21, 2022
SimDeblur is a simple framework for image and video deblurring, implemented by PyTorch

SimDeblur (Simple Deblurring) is an open source framework for image and video deblurring toolbox based on PyTorch, which contains most deep-learning based state-of-the-art deblurring algorithms. It i

220 Jan 07, 2023
Composable transformations of Python+NumPy programsComposable transformations of Python+NumPy programs

Chex Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help: Instrument your code (e.g. assertions) Debug

DeepMind 506 Jan 08, 2023
ICRA 2021 "Towards Precise and Efficient Image Guided Depth Completion"

PENet: Precise and Efficient Depth Completion This repo is the PyTorch implementation of our paper to appear in ICRA2021 on "Towards Precise and Effic

232 Dec 25, 2022
Christmas face app for Decathlon xmas coding party!

Christmas Face Application Use this library to create the perfect picture for your christmas cards! Done by Hasib Zunair, Guillaume Brassard and Samue

Hasib Zunair 4 Dec 20, 2021
3D Pose Estimation for Vehicles

3D Pose Estimation for Vehicles Introduction This work generates 4 key-points and 2 key-edges from vertices and edges of vehicles as ground truth. The

Jingyi Wang 1 Nov 01, 2021
Repository containing the PhD Thesis "Formal Verification of Deep Reinforcement Learning Agents"

Getting Started This repository contains the code used for the following publications: Probabilistic Guarantees for Safe Deep Reinforcement Learning (

Edoardo Bacci 5 Aug 31, 2022
Implementation of Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

acLSTM_motion This folder contains an implementation of acRNN for the CMU motion database written in Pytorch. See the following links for more backgro

Yi_Zhou 61 Sep 07, 2022
Official repository of the paper 'Essentials for Class Incremental Learning'

Essentials for Class Incremental Learning Official repository of the paper 'Essentials for Class Incremental Learning' This Pytorch repository contain

33 Nov 27, 2022
AAAI-22 paper: SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning

SimSR Code and dataset for the paper SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning (AAAI-22). Requirements We assum

7 Dec 19, 2022