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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LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone.
Phong Nguyen,Muhammad Arsalan,Ja Hyung Koo,Rizwan Ali Naqvi,Noi Quang Truong,Kang Ryoung Park +5 more
TL;DR: This work determined how to safely land a drone in a GPS-denied environment using the authors' remote-marker-based tracking algorithm based on a single visible-light-camera sensor that significantly outperforms state-of-the-art object trackers both using and not using convolutional neural network in terms of both accuracy and processing time.
Journal ArticleDOI
Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm.
TL;DR: It is shown that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.
Journal ArticleDOI
Facial Expressions Recognition for Human-Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer.
TL;DR: The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression recognition (FER) in order to obtain real-time inference speed for the entire process.
Journal ArticleDOI
Automatic Building Extraction from Google Earth Images under Complex Backgrounds Based on Deep Instance Segmentation Network.
TL;DR: An improved Mask Region Convolutional Neural Network (Mask R-CNN) method that can detect the rotated bounding boxes of buildings and segment them from very complex backgrounds, simultaneously is proposed.
Journal ArticleDOI
Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera
TL;DR: The proposed framework for moving vehicle detecting, tracking, and geolocation based on a monocular camera, a GPS receiver, and inertial measurement units (IMUs) sensors demonstrates its capacity in automatic supervision on target vehicles in real-world experiments, which suggests its potential applications in urban traffic, logistics, and security.