A Double Self-Supervised Model for Pitting Detection on Ball Screws

Abstract

Automatic detection of pitting on Ball Screw Drive (BSD) is essential to ensure normal production activities. However, the scarcity of defective samples and precisely labeled data poses a significant challenge. To address this, we propose an efficient double self-supervised model that operates at both the image and pixel levels, aiming to construct a high-performance model trained with defect-free data for detecting unknown defects in BSD images. By incorporating global and local information and extracting features at multiple hierarchical levels, the model’s generalization performance is enhanced. The image-level self-supervised representation is first learned by classifying normal images from the PasteNoise, a data augmentation approach by pasting noise patches at random locations in normal images. Meanwhile, the pixel-level self-supervised representation is learned by segmenting the noise patch to locate abnormal regions. Then, we introduce a novel feature masking strategy in a masking and prediction task for accurate defect localization. In addition, we use Histogram of Oriented Gradients (HOG) features with local contrast normalization as prediction targets to capture local shapes and appearances to improve the robustness of the model. The proposed method achieves competitive receiver operating characteristic curves of 97.42 (image-level) and 94.57 (pixel-level) on the BSD dataset. In experiments on the MVTec AD, the proposed model shows good performance, indicating the broad adaptability of our approach.

Keywords

• Convolutional neural network
• defect detection
• deep learning
• histogram of oriented gradients
• self-supervised learning