Year: 2026
Type: article
Abstract: Defect detection in manufacturing has evolved from manual inspection to deep learning-based Automated Visual Inspection (AVI) systems; however, acquiring sufficient defect samples in real industrial environments remains challenging, causing severe data sparsity and class imbalance. We propose CASDA (Context-Aware Steel Defect Augmentation), a five-stage framework that classifies defect morphology and background surface properties, constructs a compatibility matrix encoding their contextual relationship, and synthesizes defect images via a ControlNet pipeline conditioned on a three-channel hint image. Experiments on the Severstal steel dataset demonstrate that CASDA achieves an 83.0% quality validation pass rate. Under multi-seed evaluation (seeds 42 and 456), CASDA improved EB-YOLOv8’s overall [email protected] by 2.60 pp over the raw baseline and achieved a Class 2 AP gain of 22.09 pp over Copy-Paste, suggesting that context-aware synthesis produces more discriminative minority-class training samples than simple patch reuse under the tested settings. Performance gains are architecture-dependent; YOLO-MFD did not show overall improvement, indicating that augmentation sensitivity varies with backbone feature representation.
Source: Applied Sciences
DOI: 10.3390/app16126137
Authors: Ho-Jun Han, Il-Young Moon
Institution: Korea University of Technology and Education
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7165049978