@misc {pub6466,
	title = {Physical Markers for Learning without Labeling},
	author = {Mathias Franzius},
	year = {2025},
	month = {December},
	abstract = {Physical Markers (PMs) are simple, camera-detectable tags that enable direct interaction with physical parts for rapid data collection and labeling in dynamic factory environments. They allow operators or inspectors to attach small, machine-readable markers near specific local features such as defects, surface variations, or assembly errors. When viewed by a camera, the PM provides a unique ID and spatial reference, allowing the surrounding region to be automatically captured and labeled without digital annotation or special software. This process supports extremely fast turnaround and flexible adaptation to small or frequently changing production batches, where traditional automated inspection systems can be slow to retrain, require more training or outside services. Because PMs can be placed intuitively and under real operating conditions, they naturally collect diverse visual examples that reflect the true variability of production. The system empowers line workers to participate directly in building and refining inspection datasets, merging human insight with machine learning efficiency. PMs thus offer a low-cost, adaptive, and human-centered method for live labeling and visual QA, enabling continuous learning and quality improvement even in small-scale, customized, or rapidly evolving manufacturing processes.
},
	publisher = {Honda},
	booktitle = {Honda Technical Forum 2025}
}