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Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Р Ю Чуйков, +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.

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Multi-Scale Feature Enhanced Domain Adaptive Object Detection For Power Transmission Line Inspection

Pengyu Zhang, +5 more
- 30 Sep 2020 - 
TL;DR: A multi-scale feature enhanced domain adaptation method for cross-domain object detection of power transmission lines inspection that significantly increases the performance of the object detector in several cross-scene transmission line inspection tasks.
Journal ArticleDOI

Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts

Gregor Urban, +8 more
- 01 Jan 2021 - 
TL;DR: The use of optical coherence tomography (OCT) and automated deep learning to non‐invasively evaluate hair and follicle counts that may be used to monitor the success of hair growth therapy more accurately and efficiently are proposed.

Lightweight Detection Network for Arbitrary-Oriented Vehicles in UAV Imagery via Global Attentive Relation and Multi-Path Fusion

Jiangfan Feng, +1 more
TL;DR: This work proposes a lightweight solution to detect arbitrary-oriented vehicles under uncertain backgrounds, varied resolutions, and illumination conditions, and features a compact, lightweight design that automatically recognizes key geometric factors in the UAV images.
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Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects in Optical Remote Sensing Images

Kaihua Zhang, +1 more
- 26 Jan 2022 - 
TL;DR: In this paper , a multi-stage feature pyramid network (Multi-stage FEPN) is proposed, which can effectively solve the problems of blurring of small-scale targets and large scale variations of targets detected in optical remote sensing images.
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Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction.

Matthias Kraus, +9 more
- 22 Sep 2020 - 
TL;DR: The VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework is presented, an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment and allows the user to immerse themselves in the analysis by entering the scenario in virtual reality.
References
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Proceedings ArticleDOI

Feature Pyramid Networks for Object Detection

Tsung-Yi Lin, +5 more
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.
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Focal Loss for Dense Object Detection

Tsung-Yi Lin, +4 more
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, +2 more
- 06 Oct 2018 - 
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, +9 more
- 05 Mar 2019 - 
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

Alvaro Fuentes, +3 more
- 04 Sep 2017 - 
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.