深度学习小目标检测算法研究综述.
张艳， 张明路， 吕晓玲， 郭策… - Journal of Computer …, 2022 - search.ebscohost.com
目标检测的主要目的是在图像中快速精准地识别定位出预定义类别的目标.
而随着深度学习技术的不断发展, 检测算法在相应行业大, 中目标已达到了不错的成效 …
而随着深度学习技术的不断发展, 检测算法在相应行业大, 中目标已达到了不错的成效 …
Anchor-free convolutional network with dense attention feature aggregation for ship detection in SAR images
In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it
is urgent to develop methods with higher accuracy and faster speed for ship detection in …
is urgent to develop methods with higher accuracy and faster speed for ship detection in …
A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers
C Ieracitano, A Paviglianiti, M Campolo… - IEEE/CAA Journal of …, 2020 - ieeexplore.ieee.org
The manufacturing of nanomaterials by the electrospinning process requires accurate and
meticulous inspection of related scanning electron microscope (SEM) images of the …
meticulous inspection of related scanning electron microscope (SEM) images of the …
Learning polar encodings for arbitrary-oriented ship detection in SAR images
Y He, F Gao, J Wang, A Hussain… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Common horizontal bounding box-based methods are not capable of accurately locating
slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images …
slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images …
Multimodel feature reinforcement framework using Moore–Penrose inverse for big data analysis
Fully connected representation learning (FCRL) is one of the widely used network structures
in multimodel image classification frameworks. However, most FCRL-based structures, for …
in multimodel image classification frameworks. However, most FCRL-based structures, for …
Target detection in clutter/interference regions based on deep feature fusion for HFSWR
M Wu, L Zhang, J Niu, QMJ Wu - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
High-frequency surface wave radar (HFSWR) is of great significance for maritime detection,
but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and …
but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and …
HKPM: A hierarchical key-area perception model for HFSWR maritime surveillance
High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime
surveillance because of its low-cost maintenance and coverage of wide area. However …
surveillance because of its low-cost maintenance and coverage of wide area. However …
Semisupervised manifold regularization via a subnetwork-based representation learning model
Semisupervised classification with a few labeled training samples is a challenging task in
the area of data mining. Moore–Penrose inverse (MPI)-based manifold regularization (MR) …
the area of data mining. Moore–Penrose inverse (MPI)-based manifold regularization (MR) …
False-alarm-controllable radar target detection by differentiable neyman pearson criterion for neural network
Y Zhu, Y Li, Q Zhang - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Compared with the classical constant false alarm ratio (CFAR) detector, the neural network
(NN)-based detector has data-driven representation learning ability, which can improve the …
(NN)-based detector has data-driven representation learning ability, which can improve the …
Coarse-to-Fine Target Detection for HFSWR with Spatial-Frequency Analysis and Subnet Structure
High-frequency surface wave radar (HFSWR) is a powerful tool for ship detection and
surveillance. blackHowever, the use of pre-trained deep learning (DL) networks for ship …
surveillance. blackHowever, the use of pre-trained deep learning (DL) networks for ship …