684 research outputs found
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
Exploring the effects of daylight and glazing types on self-reported satisfactions and performances: a pilot investigation in an office
Quality-Guaranteed and Cost-Effective Population Health Profiling:A Deep Active Learning Approach
Second-Harmonic Generation and Spectrum Modulation by Active Nonlinear Metamaterial
The nonlinear properties of a metamaterial sample composed of double-layer
metallic patterns and voltage controllable diodes are experimentally
investigated. Second harmonics and spectrum modulations are clearly observed in
a wide band of microwave frequencies, showing that this kind of metamaterial is
not only tunable by low DC bias voltage, but also behaves strong nonlinear
property under a small power incidence. These properties are difficult to be
found in normal, naturally occurring materials.Comment: 14 pages, 4 figure
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