Embedded UVW Alignment Platform using Deep Learning-Based Template Matching

Abstract

In industrial engineering, "alignment" is a significant part of the production line. Therefore, an accurate and fast alignment algorithm: combined with template matching technology, it is necessary to make corrections and alignments according to the matching results. Following the plan of the partner company Contrel, this research uses NVIDIA embedded system combined with the UVW alignment hardware platform to develop an alignment system. We develop an alignment algorithm that require at least one-time detection. First, find the virtual center point of the UVW platform and convert the pixel to the millimeter unit. Second, locate the reference point. Third, find the rotation and displacement difference between the position to be corrected and the coordinate of the reference point through vector operations to control the programmable logic controller (PLC) and movement process of the stepping motors; Finally, when the distance of error between the reference points and alignment result points is less than 0.02 mm, the average of alignment times is 1.69, and it can be controlled automatically. In addition, a good template matching algorithm has a massive impact on the alignment system. In the template matching subsystem, this research proposes the modified Rotation-Scale-Translation Invariant (RST) algorithm. It adds the CUDA application interface to use GPU to accelerate the calculation to meet the needs of real-time template matching; as a result, when the source image resolution is 1.3M pixels, the cost time can be less than 100 milliseconds. Besides, due to the improvement of hardware computing power and the development of deep learning neural networks as a research trend in the computer science field. This research also tries to find the combination of deep learning for template matching, using inverse compositional spatial transformation network (IC-STN), learning the amount of object rotation, and approximating the template; we also use the CNN network to extract features from the template and source images. Goal to improve the accuracy of template matching results.

Date of Award	2021
Original language	English
Supervisor	James Jenn-Jier Lien (Supervisor) & Shu-Mei Guo (Supervisor)