Oil seal surface defect detection using superpixel segmentation and circumferential difference

Abstract

Surface defects affect the quality and safety of oil seals. It is a challenge to detect such defects in a vision system because of the unequal reflection property of oil seal surfaces and low contrast between the defect and the background. This article proposes a visual detection method (VDM) for oil seal surface defects and outlines two key issues of VDMs. First, we present a superpixel segmentation algorithm based on the significant gray level variation in the radial direction of an oil seal surface image. This image is then divided into several ring belts. Subsequently, considering the reflection inequality and low contrast, we propose a new circumferential background difference algorithm based on the small variation along the circumferential direction of the image. This algorithm eliminates the influence of the reflection inequality and improves the contrast distinction between the defects and the background. The experimental results verify the effectiveness of the proposed method with a recall and precision as high as 95.2% and 86.8%, respectively.

Lightweight edge-attention network for surface-defect detection of rubber seal rings

“…the surface of the rubber seal ring owing to fluctuation of the process conditions [2]. These surface defects greatly reduce the seal performance and directly lead to seal leakage [3]. Therefore, detection of surface defects is an essential process in production to ensure the high quality and reliability of the seal rings.…”