Abstract
The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.
Highlights
Metal planar materials are widely used in automobile manufacturing, bridge construction, aerospace, and other pillar industries, which make immense contributions to the modern social development and the betterment of life
In the task of surface defect detection, we usually evaluate the relevant methods quantitatively according to statistical results, which can be divided into four categories: true positive (TP) indicates the actual defect is detected as a defect, true negative (TN) means the actual defect is mistakenly detected as a background, false positive (FP) means the actual background is wrongly detected as a defect, and false negative (FN) indicates the actual background is detected as a background
For the surface defect detection of strip steel, Wang et al [56] proposed a feature extraction method based on Local binary pattern (LBP) that simultaneously calculates the changes of the horizontal direction, vertical direction, and two diagonal directions, by which the feature extracted has better visual recognition ability, but this method still traps in the problem of noise sensitivity
Summary
Metal planar materials (e.g., steel, aluminum, copper plates and strips, etc.) are widely used in automobile manufacturing, bridge construction, aerospace, and other pillar industries, which make immense contributions to the modern social development and the betterment of life. These images are all acquired from the real-world production line using a linear array scanning CCD camera [11]. The challenges and future research trends of defect visual inspection are discussed and prospected
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