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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YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5
TL;DR: YOLO-Tea as mentioned in this paper integrated self-attention and convolution (ACmix) to YOLOv5 to better focus on tea tree leaf diseases and insect pests.
Journal ArticleDOI
Hybrid Network Model: TransConvNet for Oriented Object Detection in Remote Sensing Images
TL;DR: This paper designs a hybrid network, TransConvNet, which integrates the advantages of CNN and self-attention-based network, pays more attention to the aggregation of global and local information, makes up for the lack of rotation invariability of CNN with strong contextual attention, and adapts to the arbitrariness of the object direction of RSIs.
Journal ArticleDOI
Multi-Oriented Object Detection in High-Resolution Remote Sensing Imagery Based on Convolutional Neural Networks with Adaptive Object Orientation Features
TL;DR: A novel HSRIs object detection method based on convolutional neural networks (CNN) with adaptive object orientation features that can more accurately detect objects with large aspect ratios and densely distributed objects than some state-of-the-art object detection methods using a horizontal bounding box.
Journal ArticleDOI
Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities
Ahmed W Moawad,David Fuentes,Mohamed Elbanan,Ahmed S Shalaby,Jeffrey R. Guccione,Serageldin Kamel,Corey T. Jensen,Khaled M. Elsayes +7 more
TL;DR: The most current AI models used in medical imaging research are reviewed, providing a brief explanation of the various models described in the literature within the past 5 years.
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Comparison of SSD and Faster R-CNN Algorithms to Detect the Airports with Data Set Which Obtained From Unmanned Aerial Vehicles and Satellite Images
TL;DR: In this article, SSD-Single Shot Multibox algorithm and Faster R-CNN algorithm were used by re-training during the determination process and the results of both algorithms were evaluated within the extend of evaluation criteria such as accuracy, sensitivity, specificity, false positive rate, false negative rate, positive pred value, F score, error rate, result and training time.
References
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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.
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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.