Surface Defect Detection of Rubber Oil Seals Based on Texture Analysis

Bharathi, S. Shankar; Radhakrishnan, N.; Priya, L

doi:10.1007/978-81-322-1000-9_19

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…In the practical production process, defects, such as dents (figure 1(a)), cracks (figure 1(b)), flow mark (figure 1(c)), and burrs (figure 1(d)), will inevitably appear on * Authors to whom any correspondence should be addressed. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Huang¹,

Hu²,

Shen³

et al. 2022

Meas. Sci. Technol.

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Detection of surface defects is an essential step to ensure the quality and reliability of rubber seal-ring products. Machine vision inspection methods have received attention from engineers because they are beneficial in terms of the detection speed or detection accuracy. It is important to simultaneously guarantee high speed and high accuracy of defect detection in practice production. Considering this problem, in this paper, we propose a YOLO-based (You Only Look Once) lightweight neural network with edge-attention fusion for defect detection of rubber rings. First, an edge-attention block is integrated into the network to improve the detection accuracy. The edge images are weighted with attention weights and summarized with feature maps in the block. Second, an involution-based feature pyramid structure is proposed to reduce the network size and computational cost by using a new involution operator, instead of a convolution operator, at the bottleneck of the feature pyramid. Comparison of the proposed network with popular YOLO networks was performed based on a rubber-ring-image dataset with 138 images. The network achieved the highest detection accuracy and fastest speed among selected lightweight networks, including YOLOv3-tiny, YOLOv5-s, and YOLOX-s. Moreover, compared with the large and highly accurate YOLOv3-spp network, the network size decreased by 93.45% and the computational cost decreased by 91.46%, while average precision (AP) only decreased by 3.41%. It can be concluded that the YOLO-based lightweight neural network reaches a good balance between detection speed and detection accuracy.

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Section: Introductionmentioning

confidence: 99%

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

Huang¹,

Hu²,

Shen³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

A neural network ensemble method for effective crack segmentation using fully convolutional networks and multi-scale structured forests

Wang

Zhang

Xiao-qin

et al. 2020

Machine Vision and Applications

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Oil seal surface defect detection using superpixel segmentation and circumferential difference

Liu

Jiang

et al. 2020

International Journal of Advanced Robotic Systems

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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.

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Surface Defect Detection of Rubber Oil Seals Based on Texture Analysis

Cited by 3 publications

References 4 publications

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

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

A neural network ensemble method for effective crack segmentation using fully convolutional networks and multi-scale structured forests

Oil seal surface defect detection using superpixel segmentation and circumferential difference

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