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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Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data
TL;DR: The results show that the detection model trained using the synthetic image dataset can effectively detect reference objects from images, and it can achieve acceptable accuracies of waterlogging depths based on the detected reference objects.
Journal ArticleDOI
One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
TL;DR: The proposed disease detection method using a convolutional neural network (CNN) with multi-scale feature fusion for maize leaf disease detection provides a feasible solution not only for the diagnosis of maize leaf diseases, but also for the detection of other plant diseases.
Proceedings ArticleDOI
Cow tail detection method for body condition score using Faster R-CNN
Xiaoping Huang,Li Xinru,Zelin Hu +2 more
TL;DR: In this article, a method of image processing and deep learning is employed to estimate cow body condition score (BCS), which is an important parameter to measure cow energy reserve for feeding management.
Journal ArticleDOI
Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition
TL;DR: The results show that the proposed method to generate samples by combining synthetic images and Generative Adversarial Network technology is indeed effective, provided that a proper Convolutional Neural Network is used to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images.
Journal ArticleDOI
Automatic Detection of Transformer Components in Inspection Images Based on Improved Faster R-CNN
Ziquan Liu,Huifang Wang +1 more
TL;DR: Results show that the improved model has an obvious advantage in accuracy, and the efficiency is significantly higher than that of manual detection, which suggests that the model is suitable for practical engineering applications.
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.
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.