手术器械的无监督缺陷检测
Unsupervised Defect Detection for Surgical Instruments
AI总结：
针对手术器械缺陷检测中纹理背景导致误检、小缺陷灵敏度低及领域迁移问题，提出结合背景掩蔽、补丁分析和高效域适应的无监督方法。
摘要：
确保手术器械的安全性需要可靠地检测视觉缺陷。然而，人工检查容易出错，现有的自动缺陷检测方法通常在自然/工业图像上训练，无法有效迁移到手术领域。我们证明，简单地应用或微调这些方法会导致问题：纹理背景引起的误检、对微小缺陷的灵敏度低，以及由于域偏移导致的器械特定特征捕获不足。为了解决这些挑战，我们提出了一种通用方法，专门针对手术器械调整无监督缺陷检测方法。通过集成背景掩蔽、基于补丁的分析策略和高效的域适应，我们的方法克服了这些限制，能够可靠地检测手术器械图像中的细微缺陷。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
