Abstract
Surface defect detection based on computer vision remains a challenging task due to the uneven illumination, low contrast and miscellaneous patterns of defects. Current methods usually present undesirable detection accuracy and lack adaptability for the various scenes. In the paper, the novel uneven illumination surface defects inspection (UISDI) method is proposed to address these issues. First, the multi-scale saliency detection (MSSD) method is proposed to construct a coarse defect map and obtain the corresponding background regions. Second, a novel background similarity prior-based intrinsic image decomposition model (BSIID) is applied to divide the defect image into a non-defective shading layer and a defective reflectance layer. An accelerated optimization solution is proposed to solve the minimization problem of the intrinsic image decomposition model. Last, the enhanced defect image is obtained by filtering the reflectance image and is then utilized to accurately segment the defect region from the coarse defect map. The experiments conducted using four real-world defect datasets demonstrate that the proposed method outperforms state-of-the-art methods.
Highlights
S URFACE quality has an important role in ensuring industrial product performance
Detection Results on rail surface discrete defect (RSDD) Dataset: Fig. 7 shows some typical defect images selected from the RSDD dataset and the corresponding inspection results.The complex background with uneven illumination is a big challenge for defect detection
multiple constraints and improved texture features (MCITF)-based on saliency detection fails to detect defects, which may treated a large amount of background regions as defect regions
Summary
Traditional defect detection is performed by human eyes, which yields low efficiency and a high missing rate. Surface defect detection based on computer vision satisfies the rapid and accuracy requirements of a production line and has been extensively applied in industrial fields. Due to the inherent characteristics of surface defects, three main challenges for the surface defect detection method exist: 1) uneven illumination: due to the change in light intensity or non-uniform distribution of the surface material, the captured defect images from actual production often exhibit uneven illumination in varying degrees; 2) low contrast: the difference between defect and background is very small, especially for the defect image with serious uneven illumination; 3) miscellaneous patterns: the size of the defect region shows diversity, even for the same category of defect, which proposes high requirements for generalization of the detection method. In response to these problems, scholars have proposed many effective solutions in the past two decades. The uneven illumination surface defect inspection methods can be classified into three categories: statistical-based approaches, filter-based approaches and model-based approaches
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