Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis Article Swipe
Lijie Guo
,
Xiaoyu Hu
,
Wenhe Liu
,
Yang Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15116338
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15116338
Automated food safety inspection systems rely heavily on the visual detection of contamination, spoilage, and foreign objects in food products. Current approaches typically require extensive labeled training data for each specific hazard type, limiting generalizability to novel or rare safety issues. We propose a zero-shot detection framework for visual food safety hazards that enables the identification of previously unseen contamination types without requiring explicit training examples. Our approach adapts and extends the Knowledge-Enhanced Feature Synthesizer (KEFS) methodology to the food safety domain by constructing a specialized knowledge graph that encodes visual safety attributes and their correlations with food categories. We introduce a Food Safety Knowledge Graph (FSKG) that models the relationships between 26 food categories and 48 visual safety attributes (e.g., discoloration, mold patterns, foreign material characteristics) extracted from food safety databases and expert knowledge. Using this graph as the prior knowledge, our system synthesizes discriminative visual features for unseen hazard classes through a multi-source graph fusion module and region feature diffusion model. Experiments on our newly constructed Food Safety Visual Hazards (FSVH) dataset demonstrate that our approach achieves 63.7% mAP in zero-shot hazard detection, outperforming state-of-the-art general zero-shot detection methods by 6.9%. Furthermore, our framework demonstrates robust generalization to fine-grained novel hazard categories while maintaining high detection performance (59.8% harmonic mean) in generalized zero-shot scenarios where both seen and unseen hazards may occur simultaneously. This work represents a significant advancement toward automated, generalizable food safety inspection systems capable of adapting to emerging visual hazards without a costly retraining process.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15116338
- https://www.mdpi.com/2076-3417/15/11/6338/pdf?version=1749173259
- OA Status
- gold
- Cited By
- 6
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411060874
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411060874Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15116338Digital Object Identifier
- Title
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Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature SynthesisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-05Full publication date if available
- Authors
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Lijie Guo, Xiaoyu Hu, Wenhe Liu, Yang LiuList of authors in order
- Landing page
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https://doi.org/10.3390/app15116338Publisher landing page
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https://www.mdpi.com/2076-3417/15/11/6338/pdf?version=1749173259Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/11/6338/pdf?version=1749173259Direct OA link when available
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Shot (pellet), Feature (linguistics), Zero (linguistics), Computer science, Materials science, Philosophy, Linguistics, MetallurgyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(KEFS) | 75 |
| abstract_inverted_index.(e.g., | 120 |
| abstract_inverted_index.Safety | 103, 169 |
| abstract_inverted_index.Visual | 170 |
| abstract_inverted_index.adapts | 68 |
| abstract_inverted_index.costly | 247 |
| abstract_inverted_index.domain | 81 |
| abstract_inverted_index.expert | 133 |
| abstract_inverted_index.fusion | 156 |
| abstract_inverted_index.hazard | 31, 150, 183, 202 |
| abstract_inverted_index.model. | 162 |
| abstract_inverted_index.models | 108 |
| abstract_inverted_index.module | 157 |
| abstract_inverted_index.region | 159 |
| abstract_inverted_index.robust | 197 |
| abstract_inverted_index.safety | 2, 39, 50, 80, 91, 118, 130, 235 |
| abstract_inverted_index.system | 143 |
| abstract_inverted_index.toward | 231 |
| abstract_inverted_index.unseen | 58, 149, 220 |
| abstract_inverted_index.visual | 9, 48, 90, 117, 146, 243 |
| abstract_inverted_index.Current | 20 |
| abstract_inverted_index.Feature | 73 |
| abstract_inverted_index.Hazards | 171 |
| abstract_inverted_index.between | 111 |
| abstract_inverted_index.capable | 238 |
| abstract_inverted_index.classes | 151 |
| abstract_inverted_index.dataset | 173 |
| abstract_inverted_index.enables | 53 |
| abstract_inverted_index.encodes | 89 |
| abstract_inverted_index.extends | 70 |
| abstract_inverted_index.feature | 160 |
| abstract_inverted_index.foreign | 15, 124 |
| abstract_inverted_index.general | 187 |
| abstract_inverted_index.hazards | 51, 221, 244 |
| abstract_inverted_index.heavily | 6 |
| abstract_inverted_index.issues. | 40 |
| abstract_inverted_index.labeled | 25 |
| abstract_inverted_index.methods | 190 |
| abstract_inverted_index.objects | 16 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.require | 23 |
| abstract_inverted_index.systems | 4, 237 |
| abstract_inverted_index.through | 152 |
| abstract_inverted_index.without | 61, 245 |
| abstract_inverted_index.achieves | 178 |
| abstract_inverted_index.adapting | 240 |
| abstract_inverted_index.approach | 67, 177 |
| abstract_inverted_index.emerging | 242 |
| abstract_inverted_index.explicit | 63 |
| abstract_inverted_index.features | 147 |
| abstract_inverted_index.harmonic | 210 |
| abstract_inverted_index.limiting | 33 |
| abstract_inverted_index.material | 125 |
| abstract_inverted_index.process. | 249 |
| abstract_inverted_index.specific | 30 |
| abstract_inverted_index.training | 26, 64 |
| abstract_inverted_index.Automated | 0 |
| abstract_inverted_index.Knowledge | 104 |
| abstract_inverted_index.databases | 131 |
| abstract_inverted_index.detection | 10, 45, 189, 207 |
| abstract_inverted_index.diffusion | 161 |
| abstract_inverted_index.examples. | 65 |
| abstract_inverted_index.extensive | 24 |
| abstract_inverted_index.extracted | 127 |
| abstract_inverted_index.framework | 46, 195 |
| abstract_inverted_index.introduce | 100 |
| abstract_inverted_index.knowledge | 86 |
| abstract_inverted_index.patterns, | 123 |
| abstract_inverted_index.products. | 19 |
| abstract_inverted_index.requiring | 62 |
| abstract_inverted_index.scenarios | 215 |
| abstract_inverted_index.spoilage, | 13 |
| abstract_inverted_index.typically | 22 |
| abstract_inverted_index.zero-shot | 44, 182, 188, 214 |
| abstract_inverted_index.approaches | 21 |
| abstract_inverted_index.attributes | 92, 119 |
| abstract_inverted_index.automated, | 232 |
| abstract_inverted_index.categories | 114, 203 |
| abstract_inverted_index.detection, | 184 |
| abstract_inverted_index.inspection | 3, 236 |
| abstract_inverted_index.knowledge, | 141 |
| abstract_inverted_index.knowledge. | 134 |
| abstract_inverted_index.previously | 57 |
| abstract_inverted_index.represents | 227 |
| abstract_inverted_index.retraining | 248 |
| abstract_inverted_index.Experiments | 163 |
| abstract_inverted_index.Synthesizer | 74 |
| abstract_inverted_index.advancement | 230 |
| abstract_inverted_index.categories. | 98 |
| abstract_inverted_index.constructed | 167 |
| abstract_inverted_index.demonstrate | 174 |
| abstract_inverted_index.generalized | 213 |
| abstract_inverted_index.maintaining | 205 |
| abstract_inverted_index.methodology | 76 |
| abstract_inverted_index.performance | 208 |
| abstract_inverted_index.significant | 229 |
| abstract_inverted_index.specialized | 85 |
| abstract_inverted_index.synthesizes | 144 |
| abstract_inverted_index.Furthermore, | 193 |
| abstract_inverted_index.constructing | 83 |
| abstract_inverted_index.correlations | 95 |
| abstract_inverted_index.demonstrates | 196 |
| abstract_inverted_index.fine-grained | 200 |
| abstract_inverted_index.multi-source | 154 |
| abstract_inverted_index.contamination | 59 |
| abstract_inverted_index.generalizable | 233 |
| abstract_inverted_index.outperforming | 185 |
| abstract_inverted_index.relationships | 110 |
| abstract_inverted_index.contamination, | 12 |
| abstract_inverted_index.discoloration, | 121 |
| abstract_inverted_index.discriminative | 145 |
| abstract_inverted_index.generalization | 198 |
| abstract_inverted_index.identification | 55 |
| abstract_inverted_index.simultaneously. | 224 |
| abstract_inverted_index.characteristics) | 126 |
| abstract_inverted_index.generalizability | 34 |
| abstract_inverted_index.state-of-the-art | 186 |
| abstract_inverted_index.Knowledge-Enhanced | 72 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.97701978 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |