Research on steel rail surface defects detection based on improved YOLOv4 network
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DOI: 10.3389/fnbot.2023.1119896
Abstract: Introduction The surface images of steel rails are extremely difficult to detect and recognize due to the presence of interference such as light changes and texture background clutter during the acquisition process. Methods To improve… read more here.
Keywords: rail defects; rail; surface; defects detection ... See more keywords