Book ChapterDOI
License Plate Recognition Using Deep FCN
Yue Wu,Jianmin Li +1 more
- pp 225-234
TLDR
This work trains a 36-class FCN on a dataset of single characters and applies it to height-normalized license plates and successfully reduces the loss in detail during end-to-end convolution.Abstract:
In our work, we concentrate on the problem of car license plate recognition after the plate has been extracted from an image. Traditional methods approach this problem as three separate steps: preprocessing, segmentation, and recognition. In this paper, we propose a unified approach that integrates these steps using a fully convolutional network. We train a 36-class FCN on a dataset of single characters and apply it to height-normalized license plates. The architecture of this model successfully reduces the loss in detail during end-to-end convolution. Finally, we extract the results from the output sequences of probabilities using a variant of the NMS algorithm. The experiments on public license plate datasets show that our approach outperforms the state-of-the-art methods.read more
Citations
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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.
Book ChapterDOI
Towards Human-Level License Plate Recognition
TL;DR: A novel LPR framework consisting of semantic segmentation and character counting, towards achieving human-level performance significantly outperforms the previous state-of-the-art methods, and achieves the accuracies of more than 99% for almost all settings.
Journal ArticleDOI
Vision-based nonintrusive context documentation for earthmoving productivity simulation
TL;DR: It is expected that the resulting daily productivity report promotes data-driven decision-making for earthmoving resource allocation, thereby improving potential for saving cost and time for earthworks with an updated resource allocation plan.
Journal ArticleDOI
Towards end-to-end car license plate location and recognition in unconstrained scenarios
Shuxin Qin,Sijiang Liu +1 more
TL;DR: An efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously is presented, that is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time.
Posted Content
Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Shuxin Qin,Sijiang Liu +1 more
TL;DR: Wang et al. as mentioned in this paper presented an efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously, which can be optimized end-to-end and work in real-time.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Proceedings ArticleDOI
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Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
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