| # SteelBench: Ethics, Privacy, and Responsible Use |
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|
| ## Source and consent |
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|
| SteelBench is constructed from CCTV footage of an operating integrated |
| steel plant, captured by 64 fixed cameras installed and maintained by |
| plant safety personnel for routine industrial-safety monitoring. The |
| research team obtained the footage under a data-use agreement that |
| permits academic publication of curated, anonymized clips for |
| non-commercial research. |
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| Workers depicted in the footage were aware of the monitoring infrastructure |
| as part of their employment terms. The original capture purpose was plant |
| safety oversight; secondary use for benchmark construction was approved |
| under the data-use agreement. **No additional consent was obtained from |
| individual workers for benchmark publication.** This is documented |
| explicitly here so users can evaluate fitness for their own |
| intended use. |
|
|
| ## Anonymization |
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|
| ### Version history |
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|
| - **v1.0** (initial release): face blur on the 10,760 JPG frames only, via MediaPipe BlazeFace. The 50 sample MP4s were **not** anonymized; manifests and annotations contained one SAIL railcar identifier and the work-area name "Go Down South Side". |
| - **v1.1** (this release, 2026-05-15): added YuNet face blur on all 50 sample MP4s; added an EasyOCR on-pixel text-blur pass on every JPG and every 5th MP4 frame; scrubbed the SAIL railcar identifier from one annotation; renamed "Go Down South Side" → "Warehouse South Side" in 6 annotations + 224 manifest rows + 1 camera-zones row. |
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| ### Methods (v1.1) |
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| **(1) Face anonymization — JPGs (carried over from v1.0).** MediaPipe long-range face detector (`solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.3)`). Detected face regions are blurred with a Gaussian kernel (99×99, σ=30) plus 20% padding. Detection rate on the released JPG set: 4.81%. The low rate is structural: cameras are 7–10 m from typical worker positions, so faces occupy <20 px and fall below the detector's reliable threshold. This distance also means faces in the unblurred majority are too small to support reliable identification. |
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| **(2) Face anonymization — sample MP4s (new in v1.1).** OpenCV YuNet (`face_detection_yunet_2023mar.onnx`, score threshold 0.5), applied to every frame of every MP4 with the same 99×99 Gaussian + 20% padding. We switched to YuNet (rather than re-using MediaPipe BlazeFace) because the MediaPipe `solutions` API was removed in the Python 3.12 wheel; YuNet ships as a 232 KB ONNX model loadable via OpenCV directly. Audio is stripped during re-encoding. Across all 50 MP4s (13,714 frames), 19,306 face-blur applications were made. |
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| **(3) On-pixel text anonymization (new in v1.1).** EasyOCR (English + Hindi/Devanagari) detects all visible text with a confidence threshold of 0.20. Detected text is blurred with a Gaussian kernel (51×51) plus 8 px padding when it matches any of: |
| - Brand/company identifiers: SAIL, BSL, Steel Authority |
| - Indian location words: Bokaro, Jharkhand, India(n), and any Devanagari (Hindi) script |
| - Other Indian steel plant names: IISCO, Durgapur, Bhilai, Rourkela, Jamshedpur |
| - Area-board signage referring to the source plant's internal zone names |
| - CCTV timestamp / camera-ID overlay patterns |
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| In addition, **all** detected text in the top 80 px and bottom 80 px of every frame is blurred regardless of content (these bands typically carry CCTV camera-ID and timestamp overlays). |
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| Across the released frame set: 12,573 sensitive-pattern regions and 10,229 overlay-band regions were blurred in 10,616 of 10,760 JPGs. In the sample MP4s (OCR run every 5th frame): 3,347 sensitive + 2,363 overlay regions were blurred. |
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| **Anonymization is best-effort, not exhaustive.** Specifically: |
| - Small faces (<20 px) may not be detected at all and therefore not |
| blurred. |
| - Side / back views and partial occlusions reduce detection rates. |
| - Helmet-occluded faces are sometimes missed. |
| - Detection precision is high (manual spot-check showed no false |
| positives in 100 sampled blurred regions), but recall is bounded by |
| the model. |
|
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| Users requiring exhaustive de-identification beyond face blurring (e.g., |
| gait, body shape, distinctive PPE) should not assume this dataset |
| provides it. |
|
|
| ## Restrictions on use |
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|
| By accessing or using SteelBench, users agree to the following beyond the |
| CC-BY-NC 4.0 base license: |
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|
| 1. **No re-identification.** Users will not attempt to identify |
| individuals depicted in the dataset. This includes (but is not limited |
| to): combining the dataset with other footage of the same site, |
| reverse-image search, biometric matching, or contacting plant |
| personnel to identify workers. |
|
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| 2. **No biometric training.** The dataset must not be used to train |
| face-recognition, gait-recognition, voice (the audio track is |
| stripped, but if recovered), or other biometric identification |
| models. |
|
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| 3. **No surveillance-product deployment.** The dataset is for VLM |
| evaluation in research contexts. Any deployment of derived models in |
| production worker-monitoring systems requires independent safety |
| validation appropriate to the deployment jurisdiction (workplace |
| surveillance laws vary widely). |
|
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| 4. **No use against protected categories.** The dataset must not be used |
| to predict or infer membership in protected categories (race, |
| religion, union activity, disability, etc.) of depicted workers. |
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| 5. **Honor the redistribution clause** in `LICENSE`: redistributors must |
| include this `ethics.md` unchanged. |
|
|
| ## Risks and limitations |
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|
| - **False-safe predictions are particularly dangerous.** The paper |
| documents that several evaluated VLMs have higher false-safe rates |
| (predicting "no violation" when one exists) than false-alarm rates. |
| Users building safety-critical systems on top of these models should |
| treat the dataset's findings as a warning sign rather than a green |
| light. |
|
|
| - **Single-facility data.** All clips originate from one integrated |
| steel plant. Sites in other facilities — different equipment, PPE |
| conventions, lighting, or worker demographics — may exhibit |
| different model behavior than what this dataset reveals. |
|
|
| - **Annotation reflects observer judgment.** Action codes and |
| safety-rule violations are observer judgments by the annotation team, |
| trained against the schema in `annotation_tool/schema_validator.py`. |
| Inter-annotator agreement and inter-expert agreement are reported in |
| the companion paper. The released annotations are not legal or |
| regulatory determinations. |
|
|
| ## Audit transparency |
|
|
| The dataset was constructed with a model-assisted annotation pipeline |
| (VLM pre-fill followed by human review). The companion paper's Section 4 |
| introduces an audit protocol that empirically bounds the influence of |
| the pre-fill on the resulting annotations, including: |
| - Override rate (fields modified by humans relative to VLM outputs) |
| - Direction analysis (productive vs harmful anchoring) |
| - Calibration on a blind subset (annotators received no VLM pre-fill) |
| - Inter-expert agreement on doubly-annotated clips |
|
|
| The full audit code and intermediate audit data are in the companion |
| code repository (`scripts/anchoring_bias_analysis.py`, |
| `scripts/compute_blind_ece.py`, `scripts/audit_and_repair_annotations.py`, |
| and `paper_reference/anchoring_analysis/*.json`). |
|
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| ## Reporting issues |
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| If you identify privacy concerns, anonymization failures, or unintended |
| uses of this dataset, please file an issue on the companion code |
| repository (link in the dataset README) or contact the authors directly |
| (author identities withheld during double-blind review; consult the |
| published paper version after acceptance). |
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