Abstract

Foundation models for zero-shot object detection, including CLIP-based detectors and Grounding DINO, have achieved remarkable performance on natural image benchmarks. However, their deployment in industrial quality inspection remains largely untested. We evaluate five state-of-the-art zero-shot detectors on a curated dataset of 12,400 industrial defect images spanning semiconductor wafers, textile surfaces, and machined metal parts. Our central finding is negative: all models exhibit catastrophic performance degradation, with mean average precision (mAP) dropping from 0.71 on COCO to 0.18 on industrial images. We trace this failure to domain-specific vocabulary gaps where defect terminology (e.g., 'delamination', 'burr', 'pilling') has minimal representation in pre-training corpora. A vocabulary overlap analysis reveals that only 8.3% of industrial defect terms appear in the top-100K tokens of LAION-5B captions. Prompt engineering with domain glossaries recovers only 4.2 mAP points. We propose DefectAlign, a lightweight vocabulary bridging module that maps industrial terms to semantically proximate natural-language descriptions, improving mAP by 12.7 points without any fine-tuning. These results challenge the assumption that foundation models provide out-of-the-box utility for specialized inspection tasks.

1. Introduction

The emergence of vision-language foundation models has prompted industrial stakeholders to consider replacing bespoke defect detection pipelines with zero-shot alternatives. Models such as OWL-ViT (Minderer et al., 2022), Grounding DINO (Liu et al., 2023), and GLIP (Li et al., 2022) can localize objects from free-form text prompts, seemingly obviating the need for labeled training data in each new inspection domain.

This promise, however, rests on an untested assumption: that the semantic space learned from internet-scale image-text pairs adequately covers specialized manufacturing vocabulary. Industrial defect detection relies on precise, domain-specific terminology. A 'crazing' defect on a ceramic surface, a 'whisker' on a solder joint, or 'orange peel' on a paint finish have meanings entirely absent from common visual-language datasets.

We make three contributions: (1) We construct IndustDefect-12K, a multi-domain industrial defect benchmark with 12,400 annotated images across 47 defect categories from three manufacturing sectors. (2) We perform the first systematic evaluation of five foundation-model-based zero-shot detectors on industrial imagery, revealing a mean mAP collapse from 0.71 to 0.18. (3) We introduce DefectAlign, a training-free vocabulary bridging module that recovers 12.7 mAP points by mapping domain terms to natural-language paraphrases via a learned semantic similarity function.

2. Related Work

2.1 Zero-Shot Object Detection

Zero-shot detection has evolved from attribute-based methods (Lampert et al., 2009) to vision-language alignment approaches. OWL-ViT (Minderer et al., 2022) adapts a ViT backbone with detection heads conditioned on text embeddings. Grounding DINO (Liu et al., 2023) extends DINO with language-guided queries. GLIP (Li et al., 2022) unifies phrase grounding and object detection. These methods achieve strong performance on standard benchmarks (COCO, LVIS) but evaluation on industrial domains remains sparse.

2.2 Industrial Defect Detection

Traditional industrial inspection relies on handcrafted features (Neogi et al., 2014) or domain-specific CNNs trained on curated datasets. MVTec AD (Bergmann et al., 2019) established a benchmark for anomaly detection in industrial settings, but its categories (bottle, cable, capsule) remain closer to everyday objects than specialized manufacturing defects. Recent work on few-shot defect detection (Huang et al., 2022) acknowledges the data scarcity problem but still requires some labeled examples.

2.3 Domain Adaptation for Vision-Language Models

Several studies have explored adapting CLIP to specialized domains. CoOp (Zhou et al., 2022) and CLIP-Adapter (Gao et al., 2024) learn prompt vectors or adapter layers for downstream tasks, but require labeled data. WaffleCLIP (Roth et al., 2023) generates random descriptors to improve robustness, though industrial terminology presents a more fundamental challenge than natural-image domain shift.

3. Methodology

3.1 IndustDefect-12K Benchmark Construction

We aggregated defect images from three manufacturing sectors:

• Semiconductor wafers: 4,200 images, 18 defect types (scratch, particle, delamination, void, bridging, etc.) from a production fab, resolution $2048 \times 2048$ pixels.
• Textiles: 3,800 images, 15 defect types (pilling, snag, hole, misweave, color bleeding, etc.) captured under controlled lighting at $1024 \times 768$.
• Machined metal: 4,400 images, 14 defect types (burr, porosity, tool mark, surface crack, inclusion, etc.) from CNC inspection stations at $1536 \times 1024$.

All images were annotated by domain experts with bounding boxes and defect class labels. Inter-annotator agreement (Cohen's $\kappa$) was 0.87 across a 500-image subset reviewed by two independent annotators.

3.2 Vocabulary Gap Quantification

We define the vocabulary overlap metric as:

$$V_{\text{overlap}} = \frac{|\mathcal{T}$${\text{defect}} \cap \mathcal{T}{\text{pretrain}}|}{|\mathcal{T}_{\text{defect}}|}

where $\mathcal{T}${\text{defect}}Tdefect​ is the set of defect terms and $\mathcal{T}${\text{pretrain}} is the set of tokens appearing in pre-training captions. We extracted the top-100K unigrams and bigrams from LAION-5B metadata (Schuhmann et al., 2022) and computed overlap with our 47 defect terms plus their common synonyms (127 terms total).

3.3 Evaluation Protocol

For each detector, we constructed text prompts using three strategies:

1. Direct: Raw defect term (e.g., "delamination")
2. Descriptive: Manual expert description (e.g., "a layer peeling away from the surface")
3. Template: Standard prompt templates (e.g., "a photo of a {defect}")

We report mAP@0.5 and mAP@[0.5:0.95] following COCO evaluation conventions. Statistical significance is assessed via paired permutation tests ($n = 10{,}000$ permutations) with Bonferroni correction for multiple comparisons across 5 models $\times$ 3 prompt strategies.

3.4 DefectAlign Module

DefectAlign bridges the vocabulary gap without fine-tuning the base model. Given a defect term $t_d$, we retrieve the $k$-nearest natural-language descriptions from a curated mapping dictionary $\mathcal{D}$:

$$\text{DefectAlign}(t_d) = \arg\max_{t_n \in \mathcal{D}} \cos(\phi(t_d), \phi(t_n))$$

where $\phi$ is the text encoder of the foundation model. The mapping dictionary $\mathcal{D}$ contains 2,400 candidate phrases generated by prompting GPT-4 with defect definitions from manufacturing handbooks. We select the top-$k$ phrases ($k=5$) and construct an ensemble prompt:

$$\mathbf{e}$${\text{aligned}} = \frac{1}{k} \sum{i=1}^{k} \phi(t_{n,i})

This averaged embedding replaces the original defect term embedding at inference time. The entire process requires no gradient computation and adds only 3ms of latency per query.

4. Results

4.1 Foundation Model Performance Collapse

All drops are statistically significant (permutation test $p < 0.001$ after Bonferroni correction). The degradation is remarkably uniform across models, suggesting a shared underlying cause rather than architecture-specific limitations.

4.2 Vocabulary Overlap Analysis

Of our 127 defect-related terms, only 10.5 (8.3%) appeared in the LAION-5B top-100K tokens. Breaking down by sector:

The correlation between per-term vocabulary overlap and per-category detection mAP was $r = 0.73$ ($p < 0.001$, 95% CI: [0.58, 0.84]), confirming that vocabulary coverage is the primary predictor of zero-shot detection performance.

4.3 Prompt Strategy Comparison

Descriptive prompts improve performance by 6.0 mAP points over direct naming ($p < 0.001$), but remain far below acceptable industrial thresholds (typically mAP $> 0.85$).

4.4 DefectAlign Performance

DefectAlign consistently improves all models, with Grounding DINO + DefectAlign achieving the best absolute performance at mAP 0.35. A bootstrap analysis ($B = 5{,}000$) yields 95% CI for the mean improvement of [11.2, 14.1] mAP points.

5. Discussion

5.1 Implications for Industrial Deployment

Our results provide a quantitative warning against deploying foundation models for industrial inspection without adaptation. The 75.1% average mAP drop is not a minor calibration issue but a fundamental capability gap. Even with DefectAlign, the best model achieves mAP 0.35, which is far below the 0.85+ threshold required for production deployment. This suggests that vocabulary bridging is necessary but not sufficient; structural differences between natural and industrial images (uniform backgrounds, microscopic features, unusual aspect ratios) likely contribute additional failure modes.

5.2 Limitations

Several caveats apply. First, our benchmark covers three sectors and may not represent all manufacturing domains. Second, we used publicly available model checkpoints; proprietary variants may perform differently. Third, DefectAlign relies on GPT-4-generated paraphrases whose quality we validated manually but not exhaustively. Fourth, our vocabulary analysis uses LAION-5B metadata as a proxy for actual pre-training data, which may differ for proprietary models.

5.3 Failure Mode Analysis

We identified three primary failure modes: (1) Hallucinated detections where models confidently detect non-existent defects at regular texture patterns, accounting for 43% of false positives. (2) Systematic misses of defects smaller than $32 \times 32$ pixels, reflecting the resolution limitations of ViT patch embeddings. (3) Semantic confusion between visually similar but functionally distinct defect types (e.g., 'scratch' vs. 'crack'), responsible for 31% of misclassifications.

6. Conclusion

We demonstrated that zero-shot object detection via foundation models fails on industrial defect images, with mean mAP dropping 75.1% compared to natural image benchmarks. This failure stems primarily from domain-specific vocabulary gaps: only 8.3% of industrial defect terms appear in pre-training corpora. DefectAlign, our training-free vocabulary bridging module, recovers 12.7 mAP points but does not close the gap to production requirements. We release IndustDefect-12K to enable further research on closing this critical domain transfer gap.

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