The Application of Deep Learning and Image Processing Technology in Laser Positioning

Abstract

In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

Crack Detection on a Retaining Wall with an Innovative, Ensemble Learning Method in a Dynamic Imaging System

“…When selecting a negative sample, in general, any non-cracked image can be selected as a negative sample, but a more reasonable approach would be to consider the actual application. From the data in Table 1, it can be seen that with the increase of the number of positive and negative samples, the accuracy rate is obviously improved [15,16,17,18]. The image that cannot be discriminated is an image that cannot be classified and discriminated in 100 test images.…”

“…In the experiments, the remote cruise routine was set to the tested area of retaining wall, and the horizontal image was instantly transmitted back to the computer during the imaging process [16,17,18,19]. When the computer received the image, the system classifier was used for detection.…”

Using Machine Learning Image Recognition for Code Reviews

“…Machine Learning and Image Recognition has been used with success in many areas. For example, Lin et al describe the successful use of deep learning for laser positioning [6]. An even more applicable subject is image processing and sentiment analysis.…”

A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

“…Image segmentation refers to dividing an image into several non-overlapping areas, based on features such as grayscale, color, texture, and shape, and making these features appear similar in the same area, but obvious differences appear between the different areas. The image segmentation method is as follows [7][8][9][10]:…”