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Abstract
Aiming at the demand for extracting the three-dimensional shapes of droplets in microelectronic packaging, life science, and some related fields, as well as the problems of complex calculation and slow running speed of conventional shape from shading (SFS) illumination reflection models, this paper proposes a Lambert–Phong hybrid model algorithm to recover the 3D shapes of micro-droplets based on the mask regions with convolutional neural network features (R-CNN) method to extract the highlight region of the droplet surface. This method fully integrates the advantages of the Lambertian model’s fast running speed and the Phong model’s high accuracy for reconstruction of the highlight region. First, the Mask R-CNN network is used to realize the segmentation of the highlight region of the droplet and obtain its coordinate information. Then, different reflection models are constructed for the different reflection regions of the droplet, and the Taylor expansion and Newton iteration method are used for the reflection model to get the final height of all positions. Finally, a three-dimensional reconstruction experimental platform is built to analyze the accuracy and speed of the algorithm on the synthesized hemisphere image and the actual droplet image. The experimental results show that the proposed algorithm based on mask R-CNN had better precision and shorter running time. Hence, this paper provides a new approach for real-time measurement of 3D droplet shape in the dispensing state.
Highlights
In the process of microelectronic high-speed dispensing, detecting the 3D shape of droplets online is a necessary precondition for studying the micro-jetting effect and realizing adaptive control of the dispensing process [1,2,3,4]
There is a huge demand for detecting the 3D shapes of droplets online in many other areas. It is useful for the droplets formed by microfluidic chips for biological and biomedical applications [5,6,7,8]. 3D shape detection enables the microfluidic chip to control the volume of the droplets more precisely, thereby improving the accuracy of the entire system
The use of three-dimensional vision to reconstruct the 3D shape of droplets emerges as a reliable method
Summary
In the process of microelectronic high-speed dispensing, detecting the 3D shape of droplets online is a necessary precondition for studying the micro-jetting effect and realizing adaptive control of the dispensing process [1,2,3,4]. It provides a new approach to solve the problem of the online detection of high-viscosity micro-droplet surface topography For this method, choosing an appropriate illumination reflection model is a problem that must be solved first. The above-mentioned hybrid illumination models effectively improve the Lambertian model’s low accuracy in the recovery of the specular reflection region. These models do not effectively deal with the difference between the diffuse reflection component and the specular reflection component in different regions. From the perspective of combinatorial optimization, combining the characteristics of each algorithm, this paper proposes a Lambert–Phong combination model that uses the Lambertian model and the Phong model to reconstruct the shape of the diffuse and highlight regions, respectively.
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