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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Improved Dual Attention for Anchor-Free Object Detection
TL;DR: An end-to-end network consisting of backbone, feature pyramid, adaptive weight assignment based on dual attention, regression, and classification is built, achieving a great increment compared with other anchor-free object detectors.
Journal ArticleDOI
WDN: A One-Stage Detection Network for Wheat Heads with High Performance
TL;DR: Based on the one-stage network framework, the wheat detection net (WDN) model was proposed for wheat head detection and counting as mentioned in this paper , where an attention module and feature fusion module were added to the backbone network and the formula for the loss function was optimized as well.
Posted Content
Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system.
TL;DR: In this model, multi-scale features from feature pyramid networks are brought in and made to adjust prediction feature map of the output, which improves the sensitivity of the entire algorithm for tiny target faces and improves the accuracy of tiny face detection in the cases of long-distance and high-density crowds.
Journal ArticleDOI
Dual-satellite integrated intelligent reconnaissance autonomous decision-making model
TL;DR: A dualsatellite integrated intelligent reconnaissance decision-making model is proposed, and a doubles satellite system is established, thereby improving the accuracy, real-time, flexibility and intelligence of space remote sensing.
Journal ArticleDOI
Weakly perceived object detection based on an improved CenterNet.
Jing Zhou,Ze Chen,Xinhan Huang +2 more
TL;DR: Experiments show that the improved CenterNet can significantly improve the average detection precision for weakly perceived objects.
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.