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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Vehicle Logo Recognition Based on Enhanced Matching for Small Objects, Constrained Region and SSFPD Network.
TL;DR: A single deep neural network based on a reduced ResNeXt model and Feature Pyramid Networks is proposed in this paper, which is named as Single Shot Feature Pyramid Detector (SSFPD).
Journal ArticleDOI
Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones
Mai Thi Giang Thanh,Ngo Van Toan,Vo Truong Nhu Ngoc,Nguyen Thu Tra,Cu Nguyen Giap,Duc Minh Nguyen +5 more
TL;DR: The clinical application of YOLOv3 and Faster R-CNN models for diagnosing dental caries via smartphone images was promising and provides a preliminary insight into the potential translation of AI from the laboratory to clinical practice.
Proceedings ArticleDOI
Rotated Faster R-CNN for Oriented Object Detection in Aerial Images
TL;DR: A Rotated Faster R-CNN to detect arbitrary oriented ground targets by adding a regression branch to predict the oriented bounding boxes for ground targets and balanced FPN is used to improve the performance of detecting small targets in high resolution aerial images.
Journal ArticleDOI
YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection
TL;DR: This research improves the components of YOLOv3 and proposes a novel military target detection algorithm (YOLO-G), which has superior detection performance and the mAP index obtained by the method is 1.2% higher than that of the latest Y OLOv5 on the premise of meeting the application requirements.
Journal ArticleDOI
A three-stage real-time detector for traffic signs in large panoramas
TL;DR: A three-stage traffic sign detector, which connects a BlockNet with an RPN–RCNN detection network, which can achieve both state-of-the-art accuracy and recall; its fastest detection speed is 102 fps.
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.