Abstract
Detecting distance between surfaces of transparent materials with large area and thickness has always been a difficult problem in the field of industry. In this paper, a method based on low‐cost TOF continuous‐wave modulation and deep convolutional neural network technology is proposed. The distance detection between transparent material surfaces is converted to the problem of solving the intersection of the optical path and the transparent material’s front and rear surfaces. On this basis, the Gray code encoding and decoding operations are combined to achieve distance detection between surfaces. The problem of holes and detail loss of depth maps generated by low‐resolution TOF depth sensors have been also effectively solved. The entire system is simple and can achieve thickness detection on the full surface area. Besides, it can detect large transparent materials with a thickness of over 30 mm, which far exceeds the existing optical thickness detection system for transparent materials.
Highlights
Distance detection between surfaces of transparent materials has always been a research hotspot in the field of industry.e traditional contact distance detection method between surfaces is the simplest and lowest cost method, such as the use of vernier calipers or micrometers. e disadvantage is that it can only detect the single surface point near the edge of the surface
Noncontact distance detection methods between surfaces are widely used, which can be roughly divided into optical and nonoptical methods. e typical capacitance method [1,2,3] is a nonoptical distance detection method between surfaces. is method is based on the principle that the transparent material causes the capacitance change to detect the distance between surfaces of the transparent material. e entire system is simple, but it is extremely susceptible to space electromagnetic interference and changes in the distributed capacitance between lines
Grating spectroscopy is designed based on the principle of grating spectroscopy [7]. e system uses white light illumination. e light obtained after being reflected by the transparent material is decomposed by the concave grating. e decomposed spectrum is received by the sensor. e data is sent to the computer for spectral analysis. e distance between the surfaces of the transparent material is obtained. e drawback of this method is that the distance detection system is very difficult to adjust and correct. e optical triangulation method [8,9,10,11] uses the principle of the difference in displacement between the Advances in Materials Science and Engineering upper and lower surfaces of the transparent material. e system is simple, convenient, and effective
Summary
Distance detection between surfaces of transparent materials has always been a research hotspot in the field of industry. To develop a method that can detect the distance between the entire surfaces of the large transparent material at a time, we refer to a variety of transparent object surface reconstruction methods. Morris and Kutulakos [14] considered the water surface reconstruction with time on this basis His method can obtain the refractive index and accurately estimate the depth and normal vector of each pixel. Is provides a new idea for our distance detection method between entire surfaces of the large transparent material. Is method uses low-cost TOF sensors and deep convolutional neural network technology to achieve distance detection between the surfaces of the large transparent material. It can effectively reduce the cost of the system. Our method can detect the large transparent material with a thickness of over 30 mm
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