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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Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images
Soon-Young Kim,Azamjon Muminov +1 more
TL;DR: Wang et al. as discussed by the authors proposed a refined version of the YOLOv7 model for detecting smoke from forest fires, which added an SPPF+ layer to the network backbone to better concentrate smaller wildfire smoke regions.
Journal Article
The comparison between various object detection algorithms
TL;DR: Highly efficient algorithms have made it possible to provide real-time object detection that can be performed by continuously moving video footages.
Journal ArticleDOI
LFF-YOLO: A YOLO Algorithm With Lightweight Feature Fusion Network for Multi-Scale Defect Detection
TL;DR: Yang et al. as discussed by the authors proposed a YOLO with lightweight feature fusion network (LFF-YOLO), which uses ShuffleNetv2 as a feature extraction network to reduce the number of parameters.
Proceedings ArticleDOI
Research on Occlusion Relationship Recognition Based on Distance Measurement of Moving Objects in Video
TL;DR: The experimental results show that distance measurement of moving objects in video is effective and feasible for occlusion relationship recognition, which can provide some help for visual technology in the field of automatic driving.
Journal ArticleDOI
Vessel identification based on automatic hull inscriptions recognition
TL;DR: The main contribution of this research is a method that can identify any type of vessel in an image that has visible inscriptions placed on the hull and must be registered in a public registry, suitable for practical implementation in onshore monitoring systems.
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).
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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.