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%.
Highlights
The round hole detection method based on deep learning image technology has not been studied too much, which mainly focuses on the recognition of images
In order to verify the effectiveness of the proposed method, several tests were performed using noise and complex images as input, and the results were compared with different circle detection methods [8]
This paper takes the advantages of deep learning image recognition technology. which combines with traditional round hole detection, and proposes a chemical composite material round hole detection technology based on deep learning images
Summary
In today’s energy system structure, traditional fossil fuels still account for a large proportion. As a circular detection technology, there are many applications in the fields of industry, robotics, and general science. In order to develop an accurate and fast circle extraction method, great efforts have been made. Traditional measurement methods are mainly based on manual operation, using various testing instruments such as universal tool microscopes, coordinate measuring machines, and other man-made measurements. Manual detection often has some small errors, which make it impossible to accurately measure the actual size of the circle. The round hole detection method based on deep learning image technology has not been studied too much, which mainly focuses on the recognition of images. Journal of Nanomaterials attention is paid to image recognition and feature extraction, and there is no special detection and extraction technology for circles
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