Unsupervised Defect Detection for Surgical Instruments
This addresses the safety-critical need for accurate defect detection in surgical instruments, but it is incremental as it adapts existing unsupervised methods to a specific domain.
The paper tackled the problem of automated visual defect detection for surgical instruments, which existing methods fail to handle effectively due to domain shift and other issues, and proposed a method that integrates background masking, patch-based analysis, and domain adaptation to enable reliable detection of fine-grained defects.
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.