43 research outputs found
Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW Radars
With its small size, low cost and all-weather operation, millimeter-wave
radar can accurately measure the distance, azimuth and radial velocity of a
target compared to other traffic sensors. However, in practice, millimeter-wave
radars are plagued by various interferences, leading to a drop in target
detection accuracy or even failure to detect targets. This is undesirable in
autonomous vehicles and traffic surveillance, as it is likely to threaten human
life and cause property damage. Therefore, interference mitigation is of great
significance for millimeter-wave radar-based target detection. Currently, the
development of deep learning is rapid, but existing deep learning-based
interference mitigation models still have great limitations in terms of model
size and inference speed. For these reasons, we propose Radar-STDA, a
Radar-Spatial Temporal Denoising Autoencoder. Radar-STDA is an efficient
nano-level denoising autoencoder that takes into account both spatial and
temporal information of range-Doppler maps. Among other methods, it achieves a
maximum SINR of 17.08 dB with only 140,000 parameters. It obtains 207.6 FPS on
an RTX A4000 GPU and 56.8 FPS on an NVIDIA Jetson AGXXavier respectively when
denoising range-Doppler maps for three consecutive frames. Moreover, we release
a synthetic data set called Ra-inf for the task, which involves 384,769
range-Doppler maps with various clutters from objects of no interest and
receiver noise in realistic scenarios. To the best of our knowledge, Ra-inf is
the first synthetic dataset of radar interference. To support the community,
our research is open-source via the link
\url{https://github.com/GuanRunwei/rd_map_temporal_spatial_denoising_autoencoder}
Efficient-VRNet: An Exquisite Fusion Network for Riverway Panoptic Perception based on Asymmetric Fair Fusion of Vision and 4D mmWave Radar
Panoptic perception is essential to unmanned surface vehicles (USVs) for
autonomous navigation. The current panoptic perception scheme is mainly based
on vision only, that is, object detection and semantic segmentation are
performed simultaneously based on camera sensors. Nevertheless, the fusion of
camera and radar sensors is regarded as a promising method which could
substitute pure vision methods, but almost all works focus on object detection
only. Therefore, how to maximize and subtly fuse the features of vision and
radar to improve both detection and segmentation is a challenge. In this paper,
we focus on riverway panoptic perception based on USVs, which is a considerably
unexplored field compared with road panoptic perception. We propose
Efficient-VRNet, a model based on Contextual Clustering (CoC) and the
asymmetric fusion of vision and 4D mmWave radar, which treats both vision and
radar modalities fairly. Efficient-VRNet can simultaneously perform detection
and segmentation of riverway objects and drivable area segmentation.
Furthermore, we adopt an uncertainty-based panoptic perception training
strategy to train Efficient-VRNet. In the experiments, our Efficient-VRNet
achieves better performances on our collected dataset than other uni-modal
models, especially in adverse weather and environment with poor lighting
conditions. Our code and models are available at
\url{https://github.com/GuanRunwei/Efficient-VRNet}
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar
Current perception models for different tasks usually exist in modular forms
on Unmanned Surface Vehicles (USVs), which infer extremely slowly in parallel
on edge devices, causing the asynchrony between perception results and USV
position, and leading to error decisions of autonomous navigation. Compared
with Unmanned Ground Vehicles (UGVs), the robust perception of USVs develops
relatively slowly. Moreover, most current multi-task perception models are huge
in parameters, slow in inference and not scalable. Oriented on this, we propose
Achelous, a low-cost and fast unified panoptic perception framework for
water-surface perception based on the fusion of a monocular camera and 4D
mmWave radar. Achelous can simultaneously perform five tasks, detection and
segmentation of visual targets, drivable-area segmentation, waterline
segmentation and radar point cloud segmentation. Besides, models in Achelous
family, with less than around 5 million parameters, achieve about 18 FPS on an
NVIDIA Jetson AGX Xavier, 11 FPS faster than HybridNets, and exceed YOLOX-Tiny
and Segformer-B0 on our collected dataset about 5 mAP and 0.7
mIoU, especially under situations of adverse weather, dark environments and
camera failure. To our knowledge, Achelous is the first comprehensive panoptic
perception framework combining vision-level and point-cloud-level tasks for
water-surface perception. To promote the development of the intelligent
transportation community, we release our codes in
\url{https://github.com/GuanRunwei/Achelous}.Comment: Accepted by ITSC 202
Self-supervised learning for point cloud data: A survey
3D point clouds are a crucial type of data collected by LiDAR sensors and widely used in transportation applications due to its concise descriptions and accurate localization. Deep neural networks (DNNs) have achieved remarkable success in processing large amount of disordered and sparse 3D point clouds, especially in various computer vision tasks, such as pedestrian detection and vehicle recognition. Among all the learning paradigms, Self-Supervised Learning (SSL), an unsupervised training paradigm that mines effective information from the data itself, is considered as an essential solution to solve the time-consuming and labor-intensive data labeling problems via smart pre-training task design. This paper provides a comprehensive survey of recent advances on SSL for point clouds. We first present an innovative taxonomy, categorizing the existing SSL methods into four broad categories based on the pretexts’ characteristics. Under each category, we then further categorize the methods into more fine-grained groups and summarize the strength and limitations of the representative methods. We also compare the performance of the notable SSL methods in literature on multiple downstream tasks on benchmark datasets both quantitatively and qualitatively. Finally, we propose a number of future research directions based on the identified limitations of existing SSL research on point clouds
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces
Autonomous driving on water surfaces plays an essential role in executing
hazardous and time-consuming missions, such as maritime surveillance, survivors
rescue, environmental monitoring, hydrography mapping and waste cleaning. This
work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset
for autonomous driving on water surfaces. Equipped with a 4D radar and a
monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather
solutions for discerning object-related information, including color, shape,
texture, range, velocity, azimuth, and elevation. Focusing on typical static
and dynamic objects on water surfaces, we label the camera images and radar
point clouds at pixel-level and point-level, respectively. In addition to basic
perception tasks, such as object detection, instance segmentation and semantic
segmentation, we also provide annotations for free-space segmentation and
waterline segmentation. Leveraging the multi-task and multi-modal data, we
conduct numerous experiments on the single modality of radar and camera, as
well as the fused modalities. Results demonstrate that 4D radar-camera fusion
can considerably enhance the robustness of perception on water surfaces,
especially in adverse lighting and weather conditions. WaterScenes dataset is
public on https://waterscenes.github.io
FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system
Natural language (NL) based vehicle retrieval is a task aiming to retrieve a
vehicle that is most consistent with a given NL query from among all candidate
vehicles. Because NL query can be easily obtained, such a task has a promising
prospect in building an interactive intelligent traffic system (ITS). Current
solutions mainly focus on extracting both text and image features and mapping
them to the same latent space to compare the similarity. However, existing
methods usually use dependency analysis or semantic role-labelling techniques
to find keywords related to vehicle attributes. These techniques may require a
lot of pre-processing and post-processing work, and also suffer from extracting
the wrong keyword when the NL query is complex. To tackle these problems and
simplify, we borrow the idea from named entity recognition (NER) and construct
FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL
descriptions of vehicle tracks, containing information such as the location,
orientation, type and colour of the vehicle. FindVehicle also adopts both
overlapping entities and fine-grained entities to meet further requirements. To
verify its effectiveness, we propose a baseline NL-based vehicle retrieval
model called VehicleFinder. Our experiment shows that by using text encoders
pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\%
recall when retrieving a target vehicle by text command on our homemade dataset
based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2
CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the
Transformer-based system. The dataset is open-source via the link
https://github.com/GuanRunwei/FindVehicle, and the implementation can be found
via the link https://github.com/GuanRunwei/VehicleFinder-CTIM
Association of impaired sensitivity to thyroid hormones with hyperuricemia through obesity in the euthyroid population
Background: Impaired sensitivity to thyroid hormones is a newly proposed clinical entity associated with hyperuricemia in the subclinical hypothyroid population. However, it is unknown whether the association exists in the euthyroid population. This study aimed to explore the association of impaired sensitivity to thyroid hormones (assessed by the thyroid feedback quantile-based index [TFQI], parametric thyroid feedback quantile-based index [PTFQI], thyrotrophic thyroxine resistance index [TT4RI] and thyroid-stimulating hormone index [TSHI]) with hyperuricemia and quantify the mediating effect of body mass index BMI in the euthyroid population. Methods: This cross-sectional study enrolled Chinese adults aged ≥ 20 years who participated in the Beijing Health Management Cohort (2008–2019). Adjusted logistic regression models were used to explore the association between indices of sensitivity to thyroid hormones and hyperuricemia. Odds ratios [OR] and absolute risk differences [ARD] were calculated. Mediation analyses were performed to estimate direct and indirect effects through BMI. Results: Of 30,857 participants, 19,031 (61.7%) were male; the mean (SD) age was 47.3 (13.3) years; and 6,515 (21.1%) had hyperuricemia. After adjusting for confounders, individuals in the highest group of thyroid hormone sensitivity indices were associated with an increased prevalence of hyperuricemia compared with the lowest group (TFQI: OR = 1.18, 95% CI 1.04–1.35; PTFQI: OR = 1.20, 95% CI 1.05–1.36; TT4RI: OR = 1.17, 95% CI 1.08–1.27; TSHI: OR = 1.12, 95% CI 1.04–1.21). BMI significantly mediated 32.35%, 32.29%, 39.63%, and 37.68% of the associations of TFQI, PTFQI, TT4RI and TSHI with hyperuricemia, respectively. Conclusions: Our research revealed that BMI mediated the association between impaired sensitivity to thyroid hormones and hyperuricemia in the euthyroid population. These findings could provide useful evidence for understanding the interaction between impaired sensitivity to thyroid hormone and hyperuricemia in euthyroid individuals and suggest the clinical implications of weight control in terms of impaired thyroid hormones sensitivity
Draft genome of the Marco Polo Sheep (<i>Ovis ammon polii</i>)
Background: The Marco Polo Sheep (Ovis ammon polii), a subspecies of argali (Ovis ammon) that is distributed mainly in the Pamir Mountains, provides a mammalian model to study high altitude adaptation mechanisms. Due to over-hunting and subsistence poaching, as well as competition with livestock and habitat loss, O. ammon has been categorized as an endangered species on several lists. It can have fertile offspring with sheep. Hence, a high-quality reference genome of the Marco Polo Sheep will be very helpful in conservation genetics and even in exploiting useful genes in sheep breeding. Findings: A total of 1022.43 Gb of raw reads resulting from whole-genome sequencing of a Marco Polo Sheep were generated using an Illumina HiSeq2000 platform. The final genome assembly (2.71 Gb) has an N50 contig size of 30.7 Kb and a scaffold N50 of 5.49 Mb. The repeat sequences identified account for 46.72% of the genome, and 20 336 protein-coding genes were predicted from the masked genome. Phylogenetic analysis indicated a close relationship between Marco Polo Sheep and the domesticated sheep, and the time of their divergence was approximately 2.36 million years ago. We identified 271 expanded gene families and 168 putative positively selected genes in the Marco Polo Sheep lineage. Conclusions: We provide the first genome sequence and gene annotation for the Marco Polo Sheep. The availability of these resources will be of value in the future conservation of this endangered large mammal, for research into high altitude adaptation mechanisms, for reconstructing the evolutionary history of the Caprinae, and for the future conservation of the Marco Polo Sheep
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead