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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Proceedings ArticleDOI
Research on the Analysis and Check of Electrical Secondary PDF Drawings Based on Deep Learning
TL;DR: Using artificial intelligence and image processing technology to analyze and identify drawings, and designing automatic checking of drawings, the quality and efficiency of drawing checking were improved.
Journal ArticleDOI
AIDM-Strat: Augmented Illegal Dumping Monitoring Strategy through Deep Neural Network-Based Spatial Separation Attention of Garbage
Yeji Kim,Jeong-Phil Cho +1 more
TL;DR: In this article , a system for tracking unlawful rubbish disposal that is based on deep neural networks is proposed, which obtains the articulation points (joints) of a dumper through OpenPose and identifies the type of garbage bag through the object detection model, You Only Look Once (YOLO), to determine the distance of the dumper's wrist to the garbage bag and decide whether it is illegal dumping.
Journal ArticleDOI
Image Processing: Facilitating Retinanet for Detecting Small Objects
TL;DR: Several modifications are made to the original Retinanet to tackle the problem of detecting small objects, and Dilated convolutional layers are added to the backbone to get fined-grained features along with semantic information.
Proceedings ArticleDOI
TLCS-Anchor: a new anchor strategy for detecting small-scale unmanned aerial vehicle
TL;DR: A new compensation strategy of anchors (TLCS-Anchor) is proposed to help detect small-scale UAVs in this paper, which could not only improve the number of anchors matched with the Uavs, but also alleviate the problem that small- scale UAV’s can’t match with enough anchors to some extent.
Journal ArticleDOI
Single-Shot Object Detection via Feature Enhancement and Channel Attention
Yi Li,Lingna Wang,Zeji Wang +2 more
TL;DR: This work proposes a feature-enhancement- and channel-attention-guided single-shot detector called the FCSSD with four modules to improve object detection performance, and achieves competitive detection performance compared with existing mainstream object detection methods.
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.
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.