Detection Method of Compound Chemical Material Hole Based on Deep Learning Image. (8th March 2022)

• Record Type: Journal Article
• Authors: Chen, Jiajian; Zu, Yuwei; Yang, Jiapeng
• Other Names: Velmurugan Palanivel Academic Editor.
• Is Part Of: Journal of nanomaterials. Volume 2022(2022)
• Publication Date: 2022-03-08
• Subjects: Nanostructured materials -- Periodicals; Nanotechnology -- Periodicals; Nanomatériaux; Nanostructured materials; Nanotechnology; Nanostructures; Nanotechnology; Periodicals; Fulltext; Internet Resources; Periodicals; 620.115
• DOI: 10.1155/2022/9966145
• Languages: English
• ISSNs: 1687-4110

Abstract

Abstract : As the degree of industrialization is getting higher and higher, the requirements for the accuracy of materials are getting higher and higher. Among them, the detection of round holes in materials is particularly important. Round hole inspection is one of the important methods for material forming and precision inspection. This paper studies the round hole detection method of composite chemical materials and aims at using deep learning image technology to provide an efficient and convenient detection method for round hole detection. This paper proposes a fast circular hole detection algorithm based on contour extraction and validity judgment. The algorithm can extract the circular holes on the material sufficiently and quickly, and the image recognition technology based on deep learning can effectively improve the accuracy and efficiency of circular hole detection. Whether it is in circular contour extraction, validity analysis, or parameter calculation, the improved algorithm has shown good results. The experimental results show that the improved algorithm is significantly better than the canny algorithm for the extraction of circular hole contours. In terms of effectiveness, the calculation time of the improved algorithm is lower than the original algorithm in different data sets, and the highest is 1.14 seconds lower than the original algorithm. The error in parameter calculation is also the lowest, and the error of a set of data is as low as 0.1%.