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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An Alternative Probabilistic Interpretation of the Huber Loss
TL;DR: An alternative probabilistic interpretation of the Huber loss is proposed, which relates minimizing the loss to minimizing an upper-bound on the Kullback-Leibler divergence between Laplace distributions, where one distribution represents the noise in the ground-truth and the other represents the Noise in the prediction.
Journal ArticleDOI
Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images.
TL;DR: A deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship is proposed.
Journal ArticleDOI
Meta-transfer learning driven tensor-shot detector for the autonomous localization and recognition of concealed baggage threats
Taimur Hassan,Muhammad Shafay,Samet Akcay,Salman H. Khan,Mohammed Bennamoun,Ernesto Damiani,Naoufel Werghi +6 more
TL;DR: A novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats.
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Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
Hyun-Ki Jung,Gi-Sang Choi +1 more
TL;DR: This study proposes an improved performance of the original YOLOv5 model, and applies the obtained data to each model, to calculate the key indicators and draw a conclusion on the best model of object detection under various conditions.
Journal ArticleDOI
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
Christian Marzahl,Marc Aubreville,Christof A. Bertram,Jason Stayt,Anne-Katherine Jasensky,Florian Bartenschlager,Marco Fragoso-Garcia,Ann Kristin Barton,Svenja Elsemann,Samir Jabari,Jens Krauth,Prathmesh Madhu,Jörn Voigt,Jenny Hill,Robert Klopfleisch,Andreas Maier +15 more
TL;DR: In this article, a deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordances (0.68 to 0.86), and inter-observer variability was moderate (Fleiss' kappa = 0.88).
References
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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.