Automated Product Boundary Defect Detection Based on Image Moment Feature Anomaly

Abstract

Electric distribution cabinets are critical components in the power distribution pipeline. Surface defect detection plays an important role in the production process. It not only guarantees product quality but also affects the brand reputation. In particular, the boundaries of metallic cabinets are more vulnerable to be damaged than other surface areas. Thus, boundary defect detection is a bottleneck problem that needs to be solved. To deal with this issue, a method based on image moment feature anomaly is developed to detect the defects on cabinet surfaces. The boundary edges from an image of the produced cabinet are first extracted using a hybrid of edge detection and boundary skeleton extraction. Then, the boundary areas are divided into small and identical size image blocks. A Gaussian distribution model of normal image blocks without defects is established. Finally, the anomaly features of image blocks with defects are extracted to identify the defect image blocks based on the Gaussian distribution model and a segmentation threshold. Two experiments are carried out. One is to determine the optimal illumination intensity for image acquisition and the optimal threshold of defect detection. The other is to evaluate the performance of the defect detection method. This developed approach can be applied in the non-destructive and effective inspection of electric distribution cabinets and provides a feasible solution for metallic product quality assurance. INDEX TERMS Boundary defect detection, electric distribution cabinets, image moment, anomaly detection.

Image Denoising and Feature Extraction of Wear Debris for Online Monitoring of Planetary Gearboxes

“…In particular, the edge details are difficult to acquire for some high-frequency band noise of the wear debris images. 2D-VMD [18], [19] can decompose wear debris images into several high-frequency components (submodes). These submodes correspond to the details and trend information of the original wear debris image.…”

“…After image preprocessing, image segmentation, transformation, and feature extraction were utilized to acquire two-dimensional (2D) morphological features of wear debris [18]. For complicated equipment working under time-varying conditions, Peng [19] developed a method based on image moment feature anomalies to detect defects on cabinet surfaces, and the anomaly features of image blocks with defects were extracted to identify defect image blocks based on the Gaussian distribution model and a segmentation threshold. Wang [20] applied an improved random Hough transform to extract texture primitives, such as lines or circles.…”

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