Journal ArticleDOI
Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning
Hendry,Hendry,Rung-Ching Chen +2 more
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TLDR
This work addresses the problem of car license plate detection using a You Only Look Once-darknet deep learning framework that uses YOLO's 7 convolutional layers to detect a single class.About:
This article is published in Image and Vision Computing.The article was published on 2019-07-01. It has received 160 citations till now.read more
Citations
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Journal ArticleDOI
Occupant-density-detection based energy efficient ventilation system: Prevention of infection transmission.
Junqi Wang,Junqi Wang,Jingjing Huang,Zhuangbo Feng,Shi-Jie Cao,Shi-Jie Cao,Fariborz Haghighat +6 more
TL;DR: Wang et al. as discussed by the authors proposed a smart low-cost ventilation control strategy based on occupant density-detection algorithm with consideration of both infection prevention and energy efficiency, which can automatically adjust between the demand-controlled mode and anti-infection mode with a self-developed low cost hardware prototype.
Journal ArticleDOI
FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public
TL;DR: Zhang et al. as mentioned in this paper proposed a novel face mask detection framework FMD-Yolo to monitor whether people wear masks in a right way in public, which is an effective way to block the virus transmission.
Journal ArticleDOI
Multinational License Plate Recognition Using Generalized Character Sequence Detection
TL;DR: This study presents a deep ALPR system designed to be applicable to multinational LPs, mainly based on the you only look once (YOLO) networks, and proposes a layout detection algorithm that can extract the correct sequence of LP numbers from multinational LLP.
Journal ArticleDOI
Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review
TL;DR: This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques, and identifies promising future directions.
Journal ArticleDOI
Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection.
Danqing Xu,Yiquan Wu +1 more
TL;DR: This work adopted DenseNet (Densely Connected Network) to enhance feature extraction capability of YOLO-V3 and achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
Object Detection with Discriminatively Trained Part-Based Models
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
Journal ArticleDOI
License Plate Recognition From Still Images and Video Sequences: A Survey
Christos-Nikolaos Anagnostopoulos,Ioannis Anagnostopoulos,I. D. Psoroulas,Vassili Loumos,Eleftherios Kayafas +4 more
TL;DR: This paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment and issues such as processing time, computational power, and recognition rate are addressed.
Journal ArticleDOI
A new deep convolutional neural network for fast hyperspectral image classification
TL;DR: A new CNN architecture for the classification of hyperspectral images is presented, a 3-D network that uses both spectral and spatial information and implements a border mirroring strategy to effectively process border areas in the image.