The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis for solving the problem of product defects in design and manufacturing. Therefore, in this paper, we propose an image semantic refinement recognition method based on causal knowledge for product surface defects. Firstly, an improved ResNet was designed to improve the image classification effect. Then, the causal knowledge graph of surface defects was constructed and stored in Neo4j. Finally, a visualization platform for causal knowledge analysis was developed to realize the causal visualization of the defects in the causal knowledge graph driven by the output data of the network model. In addition, the method is validated by the surface defects dataset.
| Authors | Zhuang, Weibin and Zhang, Taihua and Yao, Liguo and Lu, Yao and Yuan, Panliang |
|---|---|
| Year | 2022 |
| Venue | Applied Sciences (2076-3417) |
| DOI | 10.3390/app12178828 (auto-fetched · CrossRef) |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category Borderline |
| GNN Architecture | Knowledge Graph Embedding (heuristic — verify) |
| Graph Encoding | Not yet extracted from PDF |
| AEC Task | Knowledge Graph Construction (inferred from title) |
| Cohort | Peak Adoption (2022) |
| Dataset Size | 4336 images (heuristic — verify) |
|---|---|
| Implementation Framework | PyTorch, Neo4j, OWL / SPARQL (heuristic — verify) |
| Key Hyperparameters | 1000 layers (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 11% (heuristic — verify) |
| Key Finding | image semantic refinement recognition method based on causal knowledge for product surface defects (heuristic — verify) |
| Quality Assessment | 3/6 · rigor: Medium · Code/Data: ✓Baseline: ✓Reproducible: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | PyTorch · Neo4j / Cypher · RDF / OWL / SPARQL |
| Code repositories found in text | https://github.com/py2neo-org/py2neo |
| Hyperparameters (regex-detected) | layers=1000 |
| Dataset stats found | 4336 images |
| Reported metrics + values | Accuracy: 93.02% · Accuracy: 86.6% · Precision: 11% · Precision: 8.3% · Recall: 96.8% · Recall: 8.3% · Recall: 11% |
| Quality (heuristic, 6-flag) | 1/6 · rigor: Low Code/Data: ✓Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: — |
Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026