A Method for Identifying Flaws on Product Surface Based on Spatial Connectivity Domain

• Main Authors: Quanyou Zhang, Yong Feng, Bao-Hua Qiang, Yaohui Li, Qiongjie Kou
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Connectivity domain; feature; image; auto parts; flaw
• Online Access: https://ieeexplore.ieee.org/document/9521782/

In this paper, we exploit a method for identifying flaws on product surface based on spatial connectivity domain. A number of algorithms for detecting local features exist that were established to enhance the efficiency and accuracy of identifying interest features, such as AKAZE, BFSIFT, BRIEF, BRISK, ORB, SURF, SIFT and PCA-SIFT algorithm. But the data of flaws on product surface which is similar and consistent with the background intensity became a dilemma to detect the feature of image. In terms of identifying flaws on product surface, the above algorithms are not effective and accurate. Our aim is to enhance the accuracy of detecting the feature of flaws on product surface, so that the product with flaws could be accurately identified in industrial production. Therefore, we propose a method to identify flaws on product surface based on spatial connectivity domain. Compared with some other algorithms, such as the extracting texture algorithm, the detecting local feature algorithm and the identifying edge algorithm, our proposed method is more effective and accurate in detecting the local feature flaws on product surface of auto parts in automotive manufacturing factory.