Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks
Z Liu, X Gu, J Chen, D Wang, Y Chen, L Wang - Automation in Construction, 2023 - Elsevier
Pavement crack detection is critical for transportation infrastructure assessment using
ground penetrating radar (GPR). This paper describes a YOLOv3 model with four-scale …
ground penetrating radar (GPR). This paper describes a YOLOv3 model with four-scale …
The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions
The increasing need for economic, safe, and sustainable smart manufacturing combined
with novel technological enablers has paved the way for artificial intelligence (AI) and big …
with novel technological enablers has paved the way for artificial intelligence (AI) and big …
Deep-learning for radar: A survey
Z Geng, H Yan, J Zhang, D Zhu - IEEE Access, 2021 - ieeexplore.ieee.org
A comprehensive and well-structured review on the application of deep learning (DL) based
algorithms, such as convolutional neural networks (CNN) and long-short term memory …
algorithms, such as convolutional neural networks (CNN) and long-short term memory …
Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks
With an intention of improving healthcare performance, wearable technology products utilize
several digital health sensors which are classically linked into sensor networks, including …
several digital health sensors which are classically linked into sensor networks, including …
Impact of deep learning and machine learning in industry 4.0: impact of deep learning
Industry 4.0 provides emergence to what is called the “Smart Factory.” Industry 4.0 (IR 4.0) is
a growing phenomenon for automation and information sharing in industrial technology …
a growing phenomenon for automation and information sharing in industrial technology …
A novel data-driven modeling method for the spatial–temporal correlated complex sea clutter
The sea clutter, referred to as the time-varying radar backscatter from the ocean surface,
plays a significant role in marine radar development. The ocean's complex hydrodynamics …
plays a significant role in marine radar development. The ocean's complex hydrodynamics …
Shape Parameter Estimation of K-Distributed Sea Clutter Using Neural Network and Multisample Percentile in Radar Industry
In this paper, we consider the problem of robustly and accurately estimating the shape
parameters of K-distributed sea clutter in the maritime radar industry. Outliers formed by non …
parameters of K-distributed sea clutter in the maritime radar industry. Outliers formed by non …
Marine radar monitoring IoT system and case study of target detection based on PPI images
Q Zhang, Y Li, C Guo, S Yin, L Ma, Y Zhu - Expert Systems, 2024 - Wiley Online Library
To ensure the ship navigation safety, it is necessary to detect the targets in the background
of sea clutter around the ship. The most commonly used target detection equipments for ship …
of sea clutter around the ship. The most commonly used target detection equipments for ship …
An empirical model of shape parameter of sea clutter based on X-band island-based radar database
XY **a, PL Shui, YS Zhang, X Li… - IEEE Geoscience and …, 2023 - ieeexplore.ieee.org
Sea clutter modeling and parameter estimation/prediction are important basis of system
design, performance assessment, and target detection of maritime radars. In addition to …
design, performance assessment, and target detection of maritime radars. In addition to …
A convolutional neural network based approach to sea clutter suppression for small boat detection
G Li, Z Song, Q Fu - Frontiers of Information Technology & Electronic …, 2020 - Springer
Current methods for radar target detection usually work on the basis of high signal-to-clutter
ratios. In this paper we propose a novel convolutional neural network based dual-activated …
ratios. In this paper we propose a novel convolutional neural network based dual-activated …