Abstract
The impact of the illumination level on the quantitative indicators of mechanical damage of the rolled strip is investigated. To do so, a physical model experiment was conducted in the laboratory. The obtained images of defects at light levels in the range of 2–800 lx were recognized by a neural network model based on the U-net architecture with a decoder based on ResNet152. Two levels of illumination were identified, at which the total area of recognized defects increased: 50 lx and 300 lx. A quantitative assessment of the overall accuracy of defect recognition was conducted on the basis of comparison with data from images marked by an expert. The best recognition result (with Dice similarity coefficient DSC = 0.89) was obtained for the illumination of 300 lx. At lower light levels (less than 200 lx), some of the damage remained unrecognized. At high light levels (higher than 500 lx), a decrease in DSC was observed, mainly due to the fact that the surface objects are better visible and the recognized fragments become wider. In addition, more false-positives fragments were recognized. The obtained results are valuable for further adjustment of industrial systems for diagnosing technological defects on rolled metal strips.
Highlights
Metallurgy is one of the high-tech industries where optical and digital control methods for the rolled metal are actively developing
Metallurgical plants use optoelectronic systems based on the analysis of local areas of the rolled strip surface
A deep convolutional neural network model based on the U-net architecture with a decoder based on ResNet152 was used to recognize damage [11]
Summary
Metallurgy is one of the high-tech industries where optical and digital control methods for the rolled metal are actively developing. Metallurgical plants use optoelectronic systems based on the analysis of local areas of the rolled strip surface Such systems are limited in understanding the propagation mechanisms of many damages, which can be inherent in the equipment installed at a particular facility only [4,5]. The existing software systems for analyzing rolled metal require us to further develop and refine software that measures a full set of diagnostic defect signs and establishes the reasons for their occurrence [8,9]. Such systems include image recording tools, band diagnostic information, and most common tools for image pre-processing, quality improvement, and image labeling
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