Catalogue Photography as a Cold Start: Toward Deployable Carbide Burr Recognition

Catalogue Photography as a Cold Start: Toward Deployable Carbide Burr Recognition

|Authors: Abilash Philip Madavath, Chandra Yuvesh Aubeeluck, Augustin Raju, Nicolas Pyschny, Felix Hackelöer, Florian Zwanzig

|Venue: arXiv preprint | Year: 2026

|arXiv: 2609.03995v1

Abstract

Verifying that manufactured batches of milling tools or carbide rotary burrs conform to production order sheets remains a largely manual and error-prone quality assurance task. Automating this process with computer vision faces a critical cold-start constraint since no labelled imagery is available, leaving manufacturer catalogue photography as the sole source of supervision. We investigate how far catalogue supervision can support an industrial recognition pipeline under domain shift, explicitly measuring the gap between catalogue separability and performance on held-out field photographs. Our findings reveal three key insights. First, off-the-shelf frozen feature extractors do not reliably separate the two task attributes, head shape and tooth profile, motivating targeted representation learning. Second, metric learning produces near-perfect unsupervised cluster discovery on catalogue images (adjusted Rand index 0.94—0.97), but less than half of this gain transfers to field photographs. Third, the largest transfer gains do not come from model scale or representation complexity, but from simple changes that reduce domain sensitivity: converting images to grayscale (+0.22) and constraining retrieval using the known order sheet via Hungarian assignment (+0.11). We therefore treat catalogue photography as a useful cold start rather than a deployment-ready training domain, and provide empirical baselines and an evaluation protocol for catalogue-to-field transfer in precision tool manufacturing.

Key Contributions

• Verifying that manufactured batches of milling tools or carbide rotary burrs conform to productio…
• Automating this process with computer vision faces a critical cold-start constraint since no labe…
• We investigate how far catalogue supervision can support an industrial recognition pipeline under…
• Our findings reveal three key insights
• First, off-the-shelf frozen feature extractors do not reliably separate the two task attributes, …

Topics

• vision
• reinforcement-learning
• benchmark

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Madavath2026_260903995v1,
  title     = {Catalogue Photography as a Cold Start: Toward Deployable Carbide Burr Recognition},
  author    = {Abilash Philip Madavath and Chandra Yuvesh Aubeeluck and Augustin Raju and Nicolas Pyschny and Felix Hackelöer and Florian Zwanzig},
  year      = {2026},
  eprint    = {2609.03995v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url       = {https://arxiv.org/abs/2609.03995v1}
}