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Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection
Hang-Cheng Dong, Guodong Liu, Dong Ye +1
Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead…
When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
Hang-Cheng Dong, Yuhao Jiang, Yibo Jiao +5
The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reli…
Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects
Hang-Cheng Dong, Yibo Jiao, Fupeng Wei +3
Industrial surface defect inspection for sample-wise quality control (QC) must simultaneously decide whether a given sample contains defects and localize those defects spatially. I…
Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception
Hang-Cheng Dong, Lu Zou, Bingguo Liu +2
Surface defect detection plays a critical role in industrial quality inspection. Recent advances in artificial intelligence have significantly enhanced the automation level of dete…
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise
Hang-Cheng Dong, Yuhao Jiang, Yingyan Huang +4
Class activation maps are widely used for explaining deep neural networks. Due to its ability to highlight regions of interest, it has evolved in recent years as a key step in weak…