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
Citations
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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.
Posted Content
Feature Pyramid Networks for Object Detection
TL;DR: Feature pyramid networks (FPNets) as mentioned in this paper exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost.
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.
References
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Image Detector Based Automatic 3D Data Labeling and Training for Vehicle Detection on Point Cloud.
TL;DR: Li et al. as discussed by the authors proposed an effective framework to produce labeled data by using an image detector as a supervisor, and they trained the network with a simple trick to eliminate noisy labels.
Proceedings ArticleDOI
Extraction of Thermos Cup Contour Based on Optimized Edge Feature and Line Detection
Gang Wang,Hao-Yu Yang +1 more
TL;DR: The results indicate that this method, which combines feature extraction and integration of line detection, can effectively extract the main contour of the thermos cup in different scenes.
Proceedings ArticleDOI
Cascade Evolving Network for Vehicle Detection of Highway
TL;DR: A novel vehicle detection scheme via Cascade Evolving Network (CEN), designed for the highway vehicle detection dataset captured from super wide-angle lens, achieves 7-11 FPS detection speed on a moderate commercial GPU, which is much more effective than the baseline model.
Dissertation
Obrazová detekce a extrakce informací z dokladů
TL;DR: This thesis uses convolutional neural network U-Net trained to create segmentation mask to find ID card in natural scene to extract text information from photographs of identity documents using Tesseract library.
Journal ArticleDOI
A Vehicle Trajectory Extraction Method for Traffic Simulating Modeling
TL;DR: The proposed computer vision method obtains the trajectory by detecting the vehicle and then tracking the trajectory, and calculates the relevant parameters of the vehicle, such as position, speed and direction, which can be used as real driving behavior data for simulation modeling.