Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existing automated defect detection methods, typically trained on natural/industrial images, fail to transfer effectively to the surgical domain. We demonstrate that simply applying or fine-tuning these approaches leads to issues: false positive detections arising from textured backgrounds, poor sensitivity to small, subtle defects, and inadequate capture of instrument-specific features due to domain shift. To address these challenges, we propose a versatile method that adapts unsupervised defect detection methods specifically for surgical instruments. By integrating background masking, a patch-based analysis strategy, and efficient domain adaptation, our method overcomes these limitations, enabling the reliable detection of fine-grained defects in surgical instrument imagery.
@article{arxiv.2509.21561,
title = {Unsupervised Defect Detection for Surgical Instruments},
author = {Joseph Huang and Yichi Zhang and Jingxi Yu and Wei Chen and Seunghyun Hwang and Qiang Qiu and Amy R. Reibman and Edward J. Delp and Fengqing Zhu},
journal= {arXiv preprint arXiv:2509.21561},
year = {2025}
}