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

Abilash Philip Madavath, Chandra Yuvesh Aubeeluck, Augustin Raju, Nicolas Pyschny, Felix Hackelöer, Florian Zwanzig · N/A · 2026

Summary

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.

Abstract Summary

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 Points

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.

Task, Data and Protocol

We recognise a burr by two attributes: head shape $s\in\mathcal{S}$ and tooth profile $p\in\mathcal{P}$. Here $/\mathcal{S}/=9$ shapes (ZYA, KUD, SPG and six others) set by DIN 8032, and $/\mathcal{P}/=15$ cuts (pitches Z1–Z5 alongside material-specific geometries such as INOX or ALU) set by DIN 8033 [ 5 ] .

Full article numbers also specify diameter and shank length, but those are scale-dependent and better resolved by camera calibration, a reference marker in the scene, or the order sheet itself.

Task, Data and Protocol — Catalogue Photography as a Cold Start: Toward Deployable Carbide Burr Recognition — Fig. 2 : UMAP projection of catalogue embeddings from the trained two-stream model (A4), coloured by ground-truth label. Each stream forms tight, well-separated groups for its own attribute.

Four Stages

We first asked whether generic pretrained backbones separate burr morphology out of the box. We ran $41$ feature extractors through six clustering algorithms ($k$-means, GMM, agglomerative with Ward linkage, HDBSCAN and two others) on each attribute, covering self-supervised transformers [ 6 , 7 ] , supervised ImageNet CNNs and ViTs [ 8 , 9 ] , and a geometric contour descriptor.

Sources and Demonstrations

Figures are reproduced from Madavath et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.