ArticleSensors (Basel, Switzerland)2019
High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM).
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Gap Measurement Method for Railway Switch Machines Based on the Fusion of Deep Vision and Geometric Features.Sensors (Basel, Switzerland) · 2026Article
- High Precision Visual Dimension Measurement Method with Large Range Based on Multi-Prism and M-Array Coding.Sensors (Basel, Switzerland) · 2022Article
- Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm.Computational intelligence and neuroscience · 2021Article
- Analysis and Research on Human Movement in Sports Scene.Computational intelligence and neuroscience · 2021Article
- Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions.Diagnostics (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In modern industries, high precision dimensional measurement plays a pivotal role in product inspection and sub-pixel edge detection is the core algorithm. Traditional interpolation and moment methods have achieved some success. However, those methods still have shortcomings. For example, the accuracy is still insufficient with the resolution limitation of the image sensor. Moreover, prediction results can be affected by image noise. With the recent success of deep learning technology, we propose a sub-pixel edge detection method based on convolution neural network (CNN) and bi-directional long short-term memory (LSTM). First, one-dimensional visual geometry group-16 (VGG-16) is employed to extract edge features. Then, a transformation operation is developed to generate sequence information. Lastly, bi-directional LSTM with fully-connected layers is introduced to output edge positions. Experimental results on our steel plate dataset demonstrate that our method achieves superior accuracy and anti-noise ability than traditional methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.