A dataset for the 2TX-4RX MMWave Radar with the raw ADC data being recorded. Six main objects - pedestrian, cyclist, car, motorbike, bus, truck - were collected to fit the automotive object detection scenario.
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition,
Xiangyu Gao, Guanbin Xing, Sumit Roy, and Hui Liu,
arXiv technical report (arXiv 2011.08981)
@ARTICLE{9249018, author={Gao, Xiangyu and Xing, Guanbin and Roy, Sumit and Liu, Hui},
journal={IEEE Sensors Journal},
title={RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition},
year={2021}, volume={21}, number={4}, pages={5119-5132}, doi={10.1109/JSEN.2020.3036047}}
Raw ADC Data of 77GHz MMWave radar for Automotive Object Detection,
Xiangyu Gao, Youchen Luo, Guanbin Xing, Sumit Roy, Hui Liu,
IEEE Dataport
@data{xm40-jx59-22, doi = {10.21227/xm40-jx59}, url = {https://dx.doi.org/10.21227/xm40-jx59},
author = {Gao, Xiangyu and Luo, Youchen and Xing, Guanbin and Roy, Sumit and Liu, Hui},
publisher = {IEEE Dataport},
title = {Raw ADC Data of 77GHz MMWave radar for Automotive Object Detection},
year = {2022} }
(Dec. 11, 2022) Initial release of dataset and tools.
Any questions or suggestions are welcome!
Xiangyu Gao xygao@uw.edu
In this dataset, we provided the raw analog-to-digital-converter (ADC) data of a 77GHz mmwave radar for the automotive object detection scenario. The overall dataset contains approximately 19800 frames of radar data as well as synchronized camera images and labels. For each radar frame, its raw data has 4 dimension: samples (fast time), chirps (slow time), transmitters, receivers. The experiment radar was assembled from the TI AWR 1843 board, with 2 horizontal transmit antennas and 4 receive antennas. With time-division multiplexing on all transmitters, it can form a 1D-MIMO virtual array with 8 elements.
The data collection was done on the campus, road, and parking lot during the daytime, with the focus of capturing the data for six main objects: pedestrian, cyclist, car, motorbike, bus, truck. The collected objects can be either moving (mostly) or static. A single data collection run consisted of multiple objects listed above moving or being static at a normal speed for 30 seconds in front of the testbed. More information in terms of dataset structure, format, tools, and radar configuration was described below.
Download dataset from the google drive link:
https://drive.google.com/file/d/1QgjwdQpY96NAVGdvjjFrXLhb48o15EO_/view?usp=share_link
Or from IEEE Dataport:
https://ieee-dataport.org/documents/raw-adc-data-77ghz-mmwave-radar-automotive-object-detection
The dataset consists of multiple sequences, e.g., "2019_04_09_bms1000", "2019_04_09_cms1000". Under each sequence folder, there exists the image folder "images_0", and radar data folder "radar_raw_frame", and label folder "text_labels".
The overall dataset structure is presented as below.
Automotive
---2019_04_09_bms1000
---images_0
---radar_raw_frame
---text_labels
---2019_04_09_cms1000
......
The "radar_raw_frame" folder contains raw ADC radar data in *.mat format, and "images_0" folder contains camera images in *.jpg format, and "text_labels" contains label files for each frame in *.csv format. The detailed data format is explained below.
-
For each radar frame, its raw data (*.mat) has 4 dimension: samples (128), chirps (255), receivers (4), transmitters (2). All transmitters were arranged with time-division multiplexing (TDM), i.e., send chirp signal one by one.
The example frame structure is shown as below:
- The placement of 2 transmitters and 4 receivers were plotted in the left figure below, from the TI documentation. Through TDM, the it forms a 1 by 8 MIMO array as shown in the right figure below:
- All radar configurations are included in config.
- The camera image for each frame is with 1440x1080 pixels.
-
Each *.csv file include the labels for a frame, with each row of it in format of [uid, class, px, py, wid, len], where uid is the unique tracking id of objects in this sequence, class is the class id of objects, with the id number represents below, px, py, wid, len are the x center, y center, width, and length of the bounding box for objects.
label_map = {0: 'person', 2: 'car', 3: 'motorbike', 5: 'bus', 7: 'truck', 80: 'cyclist', }
Python 3.6 (please refer to INSTALL to set up libraries.)
Under prepare...
This tool is release under MIT license (see LICENSE).
This project was supported by the FUNLAB, University of Washington, and Silkwave Holdings. This project is not possible without multiple great opensourced codebases. We list some notable examples below.