Cross-Scenario Industrial Defect Detection via Domain-Robust Training and Metric-Aligned Selection

Key points

• Automated defect detection of industrial boards must remain highly reliable under cross-scenario variations in lighting, surface contaminants, and production line conditions.1
• This makes cross-scenario defect detection a domain generalization problem, wherein models are required to detect, localize, and classify defects across unseen scenarios while minimizing false alarms on clean boards.1
• These four modules respectively introduce cross-scenario robust normalization and DSU-style statistic perturbation with progressive random convolution, NWD and parameter-free spatial attention, the VFL learning objective, and a custom SWAD variant driven by the Track 1-oriented proxy.1
• Experimental results demonstrate that our model achieves a Track 1 score of 0.564 with a screening score of 0.696 on the test dataset on a single RTX 4090 GPU.1

Sources (1)

• [1]Cross-Scenario Industrial Defect Detection via Domain-Robust Training and Metric-Aligned Selection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:57 AM

  For the IDA 2026 Track 1 challenge, we propose a cross-scenario defect detection method that includes four complementary modules: cross-scenario robust training, small-defect-aware learning, score calibration and inference threshold selection, and metric-aligned checkpoint selection.

  Automated defect detection of industrial boards must remain highly reliable under cross-scenario variations in lighting, surface contaminants, and production line conditions.

Extractive summary: sentences quoted from the sources.