Steel ball surface inspection using modified DRAEM and machine vision

Abstract

Precision steel balls are among the most crucial components in the industry, widely used in various equipment related to bearings, such as CNC, automotive, medical, and machinery industries. Due to the reflective surface of steel balls, flaw inspection becomes a challenging task. This paper introduces an automatic optical inspection system that employs a modified DRAEM, a reconstruction-based anomaly detection network, for examining the surface of precision steel balls. We made three modifications to the DRAEM network (Zavrtanik, V., Kristan, M., & Skoca, D. (2021). DRAEM—a discriminatively trained reconstruction embedding for surface anomaly detection. http://arXiv.org/arXiv:2108.07610[cs.CV]), including adjusting the generation process of synthesized anomalies, adding a few skip connections from the encoder to the decoder, and incorporating an attention module to enhance the quality of reconstructed images and reduce misjudgments. Experimental results demonstrate a reduction in the model's underkill rate from 8.8% to 4.6% and the overkill rate from 1.5% to 0.4%. This indicates that the proposed methods addressed the issues of reconstruction distortion and the inability to detect small and inconspicuous defects. The proposed system has been successfully implemented in a case study company, showcasing significant advantages, particularly in scenarios involving new production lines or a lack of sufficient defective samples for collection.