High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM)
1
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
3
Scoop Medical, Houston, TX 77007, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5302; https://doi.org/10.3390/s19235302
Received: 29 September 2019 / Revised: 15 November 2019 / Accepted: 25 November 2019 / Published: 2 December 2019
(This article belongs to the Section Physical Sensors)
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.
View Full-Text
Keywords:
dimensional measurement; sub-pixel edge detection; deep learning; convolutional neural network; bi-directional LSTM
▼
Show Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Wang, Y.; Chen, Q.; Ding, M.; Li, J. High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM). Sensors 2019, 19, 5302.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
