Open Access
Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector
Р Ю Чуйков,Д А Юдин +1 more
- Vol. 2, Iss: 4
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The article was published on 2017-01-01 and is currently open access. It has received 1687 citations till now.read more
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Research Progress on Aircraft Detection and Recognition in SAR Imagery
Guo Qian,Wang Haipeng,Xu Feng +2 more
TL;DR: It is proposed that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance.
Journal ArticleDOI
DeepPrimitive: Image decomposition by layered primitive detection
Jiahui Huang,Jun Gao,Vignesh Ganapathi-Subramanian,Hao Su,Yin Liu,Chengcheng Tang,Leonidas J. Guibas +6 more
TL;DR: This paper builds a framework to detect primitives from images in a layered manner by modifying the YOLO network, and uses an RNN with a novel loss function to equip this network with the capability to predict primitives with a variable number of parameters.
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Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images.
TL;DR: An object detection algorithm by jointing semantic segmentation (SSOD) for images is proposed by constructing a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information.
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WCNN3D: Wavelet Convolutional Neural Network-Based 3D Object Detection for Autonomous Driving
TL;DR: A wavelet-multiresolution-analysis-based 3D object detection network without a pooling operation that outperforms the PointPillars-based model by up to 14% while reducing the number of trainable parameters.
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An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection
TL;DR: The proposed YOLO-UOD is deployed on the embedded system Jetson Nano 2 GB with a detection speed of 9.24 FPS, which shows that it can detect effectively in scenarios with limited computing power.
References
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Proceedings ArticleDOI
Feature Pyramid Networks for Object Detection
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Proceedings ArticleDOI
Focal Loss for Dense Object Detection
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
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SECOND: Sparsely Embedded Convolutional Detection
Yan Yan,Yuxing Mao,Bo Li +2 more
TL;DR: An improved sparse convolution method for Voxel-based 3D convolutional networks is investigated, which significantly increases the speed of both training and inference and introduces a new form of angle loss regression to improve the orientation estimation performance.
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A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
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A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
TL;DR: A deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions, and combines each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network.