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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Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities
P. Ingle,Young-Gab Kim +1 more
TL;DR: A resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment is proposed.
Journal ArticleDOI
A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection
Seong-heum Kim,Youngbae Hwang +1 more
TL;DR: This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection, and presents the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions.
Proceedings ArticleDOI
Research on FOD Detection for Airport Runway based on YOLOv3
Peng Li,Huajian Li +1 more
TL;DR: A detection algorithm based on YOLOv3(You Only Look Once) for foreign objects debris is presented that employs deep residual network to extract feature and multi-scale feature fusion to detect small-scale FOD.
Journal ArticleDOI
Object Detection in Sonar Images
TL;DR: A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images, which shows an overall detection and classification performance average precision (AP) score of 0.98392.
Journal ArticleDOI
Joint Banknote Recognition and Counterfeit Detection Using Explainable Artificial Intelligence
Miseon Han,Jeongtae Kim +1 more
TL;DR: This work investigates machine learning-based joint banknote recognition and counterfeit detection method and proposes an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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).
Journal ArticleDOI
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.