This invention discloses an
industrial material inspection method and
system based on
feature extraction and
contrast enhancement, belonging to the field of
image inspection technology. It includes: S1, acquiring the transmitted
signal using a digital
flat panel detector and generating an original
digital image based on the differences in X-
ray absorption by different materials or defects; S2, performing uniformity correction and normalization enhancement on the original
digital image, and achieving preliminary differentiation of different material regions; S3, dividing the original image data into
processing domains based on the preliminary differentiation results, and performing differentiated enhancement
processing based on the local feature attributes of each
processing domain to generate a binary feature image. By integrating X-
ray penetration imaging, multi-dimensional
image enhancement, and a
deep learning-driven intelligent
rating system, this invention fundamentally solves the key technical bottlenecks in traditional
industrial material inspection, such as inconsistent
image quality, lack of material identification, and strong subjectivity in rating, achieving an automated, intelligent, and objective
upgrade in
industrial material defect detection.