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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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.
Posted Content
Feature Pyramid Networks for Object Detection
TL;DR: Feature pyramid networks (FPNets) as mentioned in this paper exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost.
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.
References
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Journal ArticleDOI
Multi-Object Tracking with Correlation Filter for Autonomous Vehicle.
TL;DR: This paper improves the detection module by incorporating the temporal information, which is beneficial for detecting small objects, and proposes a novel compressed deep Convolutional Neural Network feature based Correlation Filter tracker for the tracking module.
Journal ArticleDOI
Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis
TL;DR: This work implements the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality and uses SPP for more comprehensive learning of multiscale object features.
Journal ArticleDOI
Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method.
TL;DR: Mixed YOLOv3-LITE is proposed, a lightweight real-time object detection network that can be used with non-graphics processing unit (GPU) and mobile devices and can achieve higher efficiency and better performance on mobile terminals and other devices.
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
TL;DR: A novel approach is proposed, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection, and shows that re-scaling the image to a lower resolution will sometimes produce better accuracy.
Journal ArticleDOI
A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization
TL;DR: A lightweight deep-learning-based object detection approach for mobile or embedded devices that achieves a high detection accuracy, stable geovisualization results and interaction and is independent of the network to ensure robustness to poor signal conditions.