138 research outputs found
Road Disease Detection based on Latent Domain Background Feature Separation and Suppression
Road disease detection is challenging due to the the small proportion of road
damage in target region and the diverse background,which introduce lots of
domain information.Besides, disease categories have high similarity,makes the
detection more difficult. In this paper, we propose a new LDBFSS(Latent Domain
Background Feature Separation and Suppression) network which could perform
background information separation and suppression without domain supervision
and contrastive enhancement of object features.We combine our LDBFSS network
with YOLOv5 model to enhance disease features for better road disease
detection. As the components of LDBFSS network, we first design a latent domain
discovery module and a domain adversarial learning module to obtain pseudo
domain labels through unsupervised method, guiding domain discriminator and
model to train adversarially to suppress background information. In addition,
we introduce a contrastive learning module and design k-instance contrastive
loss, optimize the disease feature representation by increasing the inter-class
distance and reducing the intra-class distance for object features. We
conducted experiments on two road disease detection datasets, GRDDC and CNRDD,
and compared with other models,which show an increase of nearly 4% on GRDDC
dataset compared with optimal model, and an increase of 4.6% on CNRDD dataset.
Experimental results prove the effectiveness and superiority of our model
Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection
In the domain of traffic safety and road maintenance, precise detection of
road damage is crucial for ensuring safe driving and prolonging road
durability. However, current methods often fall short due to limited data.
Prior attempts have used Generative Adversarial Networks to generate damage
with diverse shapes and manually integrate it into appropriate positions.
However, the problem has not been well explored and is faced with two
challenges. First, they only enrich the location and shape of damage while
neglect the diversity of severity levels, and the realism still needs further
improvement. Second, they require a significant amount of manual effort. To
address these challenges, we propose an innovative approach. In addition to
using GAN to generate damage with various shapes, we further employ texture
synthesis techniques to extract road textures. These two elements are then
mixed with different weights, allowing us to control the severity of the
synthesized damage, which are then embedded back into the original images via
Poisson blending. Our method ensures both richness of damage severity and a
better alignment with the background. To save labor costs, we leverage
structural similarity for automated sample selection during embedding. Each
augmented data of an original image contains versions with varying severity
levels. We implement a straightforward screening strategy to mitigate
distribution drift. Experiments are conducted on a public road damage dataset.
The proposed method not only eliminates the need for manual labor but also
achieves remarkable enhancements, improving the mAP by 4.1% and the F1-score by
4.5%.Comment: 10 pages, 13 figures, 2 Table
MFL-YOLO: An Object Detection Model for Damaged Traffic Signs
Traffic signs are important facilities to ensure traffic safety and smooth
flow, but may be damaged due to many reasons, which poses a great safety
hazard. Therefore, it is important to study a method to detect damaged traffic
signs. Existing object detection techniques for damaged traffic signs are still
absent. Since damaged traffic signs are closer in appearance to normal ones, it
is difficult to capture the detailed local damage features of damaged traffic
signs using traditional object detection methods. In this paper, we propose an
improved object detection method based on YOLOv5s, namely MFL-YOLO (Mutual
Feature Levels Loss enhanced YOLO). We designed a simple cross-level loss
function so that each level of the model has its own role, which is beneficial
for the model to be able to learn more diverse features and improve the fine
granularity. The method can be applied as a plug-and-play module and it does
not increase the structural complexity or the computational complexity while
improving the accuracy. We also replaced the traditional convolution and CSP
with the GSConv and VoVGSCSP in the neck of YOLOv5s to reduce the scale and
computational complexity. Compared with YOLOv5s, our MFL-YOLO improves 4.3 and
5.1 in F1 scores and mAP, while reducing the FLOPs by 8.9%. The Grad-CAM heat
map visualization shows that our model can better focus on the local details of
the damaged traffic signs. In addition, we also conducted experiments on
CCTSDB2021 and TT100K to further validate the generalization of our model.Comment: 11 pages, 8 figures, 4 table
Tryptophan-based carbon dots as fluorescent probe for detection of Pb2+ and Fe3+ ions
A Probe for metal ions based on carbon dots (CDs) has been prepared. A one-step method has been developed to synthesize the probe using tryptophan as the recognizing group. The synthesized probe has been evaluated for metal ions’ detection. The results show increase in fluorescence in the presence of Pb2+, over other 14 metal ions, illustrating the selective and sensitive detection of Pb2+
Impact of meteorological factors on the COVID-19 transmission: A multicity study in China
The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID- 19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below0 °C, 12 cities had average AHbelow4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition,we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission
LAMOST Observations in the Kepler Field. II. Database of the Low-resolution Spectra from the Five-year Regular Survey
The LAMOST-Kepler (LK-) project was initiated to use the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) to make spectroscopic follow-up observations for the targets in the field of the Kepler mission. The Kepler field is divided into 14 subfields that are adapted to the LAMOST circular field with a diameter of 5°. During the regular survey phase of LAMOST, the LK-project took data from 2012 June to 2017 June and covered all 14 subfields at least twice. In particular, we describe in this paper the second Data Release of the LK-project, including all spectra acquired through 2015 May-2017 June together with the first round observations of the LK-project from 2012 June to 2014 September. The LK-project now counts 227,870 spectra of 156,390 stars, among which we have derived atmospheric parameters ({log}g, T eff, and [Fe/H]) and heliocentric radial velocity for 173,971 spectra of 126,172 stars. These parameters were obtained with the most recent version of the LAMOST Stellar Parameter Pipeline v 2.9.7. Nearly one half, namely 76,283 targets, are observed both by the LAMOST and Kepler telescopes. These spectra, establishing a large spectroscopy library, will be useful for the entire astronomical community, particularly for planetary science and stellar variability on Kepler targets. Based on observations collected with the Large Sky Area Multi-Object Fiber spectroscopic Telescope (LAMOST), which is located at the Xinglong Observatory, China
Spin-orbit-coupled triangular-lattice spin liquid in rare-earth chalcogenides
Spin-orbit coupling is an important ingredient in many spin liquid candidate
materials, especially among the rare-earth magnets and Kitaev materials. We
explore the rare-earth chalcogenides NaYbS where the Yb ions form a
perfect triangular lattice. Unlike its isostructural counterpart YbMgGaO
and the kagom\'{e} lattice herbertsmithite, this material does not have any
site disorders both in magnetic and non-magnetic sites. We carried out the
thermodynamic and inelastic neutron scattering measurements. The magnetic
dynamics could be observed with a broad gapless excitation band up to 1.0 meV
at 50 mK and 0 T, no static long-range magnetic ordering is detected down to 50
mK. We discuss the possibility of Dirac spin liquid for NaYbS. We identify
the experimental signatures of field-induced transitions from the disordered
spin liquid to an ordered antiferromagnet with an excitation gap at finite
magnetic fields and discuss this result with our Monte Carlo calculation of the
proposed spin model. Our findings could inspire further interests in the
spin-orbit-coupled spin liquids and the magnetic ordering transition from them
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image
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