发表机构
Hebei University of Technology; Lappeenranta-Lahti University of Technology（河北工业大学; 拉彭兰塔-拉赫蒂理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于测试时自适应的ABDD框架，通过双对齐策略和不确定性感知过滤，提升航空发动机叶片稀疏缺陷在域偏移下的检测鲁棒性。
AI 中文摘要
可靠的视觉检测对于航空发动机叶片制造中的质量保证至关重要，其中缺陷外观可能因生产线、成像条件、叶片姿态和表面背景的不同而变化。这种域偏移导致训练数据与部署数据之间的不匹配，并降低深度缺陷检测器在线检测的可靠性。该问题尤其具有挑战性，因为航空发动机叶片图像通常包含稀疏缺陷，使得基于伪标签的自适应容易受到噪声或缺失预测的影响。为解决此问题，我们提出航空发动机叶片缺陷检测器（ABDD），一种基于测试时自适应的在线自适应检测框架。ABDD引入双对齐策略，通过结合特征统计对齐与伪框对齐，联合适应全局视觉风格和局部缺陷形态。为减少不可靠伪标签引起的错误累积，一种不确定性感知框过滤机制利用分类置信度、分类熵和定位熵评估伪框。此外，轻量级稀疏膨胀模块（Sparse Dilated Mona）支持参数高效的增量调整，同时限制源域遗忘。ABDD在CD-AeBD和HD-AeBD上于多种域偏移场景下进行评估，并在统一的RT-DETR + Swin-T架构下比较TTA策略。实验表明，ABDD在域偏移下持续提升检测鲁棒性，其实用性进一步在工业检测平台上得到验证。
英文摘要
Reliable visual inspection is essential for quality assurance in aero-engine blade manufacturing, where defect appearance may vary across production lines, imaging conditions, blade poses, and surface backgrounds. Such domain shifts cause a mismatch between training and deployment data and degrade the reliability of deep defect detectors in online inspection. This problem is particularly challenging because aero-engine blade images usually contain sparse defects, making pseudolabel-based adaptation vulnerable to noisy or missing predictions. To address this issue, we propose Aero-engine Blade Defect Detector (ABDD), an online adaptive detection framework based on test-time adaptation. ABDD introduces a Dual-Alignment Strategy to jointly adapt global visual style and local defect morphology by combining feature-statistics alignment with pseudo-box alignment. To reduce error accumulation from unreliable pseudo labels, an Uncertainty-aware Box Filtering mechanism evaluates pseudo boxes using classification confidence, classification entropy, and localization entropy. In addition, a lightweight Sparse Dilated Mona module enables parameter-efficient delta tuning while limiting source-domain forgetting. ABDD is evaluated on CD-AeBD and HD-AeBD under multiple domain-shift scenarios, with TTA strategies compared under a unified RT-DETR + Swin-T architecture. Experiments show that ABDD consistently improves detection robustness under domain shifts, and its practicality is further validated on an industrial inspection platform.
CommentsThis manuscript is Accepted at conference PRCV 2026
