| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - video-classification |
| - image-classification |
| - visual-question-answering |
| tags: |
| - industrial |
| - safety |
| - surveillance |
| - vlm-evaluation |
| - action-recognition |
| pretty_name: SteelBench |
| configs: |
| - config_name: clips |
| description: Per-clip metadata for all 1,345 GT clips (site, work_area, camera_id, BRISQUE, person-detection statistics, severity tier, visibility condition). |
| data_files: |
| - split: full |
| path: manifests/batch_manifest.csv |
| - split: sample |
| path: sample/sample_manifest.csv |
| --- |
| |
| # SteelBench: A Diagnostic Benchmark for Vision-Language Models in Industrial Safety Monitoring |
|
|
| SteelBench is a diagnostic benchmark of densely annotated CCTV clips from an |
| operating integrated steel plant. It is designed to evaluate vision-language |
| models (VLMs) on real-world industrial action recognition, PPE assessment, |
| and safety-violation detection — under naturally occurring degradation |
| (dust, glare, steam, low light), at distances and crowdedness levels that |
| curated benchmarks miss. |
|
|
| The benchmark is paired with an **audit protocol** that empirically bounds |
| the influence of model-assisted annotation on evaluation results. The |
| protocol is itself a methodological contribution: a reusable recipe for |
| provenance-aware dataset construction in domains where annotation is |
| scarce or expensive. |
|
|
| ## At a glance |
|
|
| | | | |
| |---|---| |
| | Clips | 1,345 (15 s each, 1080p, H.264) | |
| | Frames | 10,760 evenly-spaced JPEGs (8 per clip) at quality 95 | |
| | Sites | 16 across an integrated steel plant (ASP, BF, CRM, RED, RERS, SMS, etc.) | |
| | Action taxonomy | 25 codes in 6 groups (A–F) + X1 (unlisted) | |
| | PPE items | 5 per worker (helmet, high-vis vest, safety shoes, welding protection, harness) | |
| | Safety rules | 55 general (UA-G) + site-specific (UA-SP, UA-RED, UA-CRM, …) | |
| | Visibility conditions | 6 (clear, steam, dust, smoke, low_light, glare) | |
| | Annotation layers | Layer 1 (scene-level, >5 workers) / Layer 2 (per-person, ≤5 workers) | |
| | Annotators | 5 active tier-1 annotators + 2 experts + 1 safety officer | |
| | Ground truth provenance | Highest-priority annotation per clip (expert > tier-1 > safety_officer) | |
| | License | CC-BY-NC 4.0 (non-commercial, attribution) | |
|
|
| ## Dataset structure |
|
|
| ``` |
| SteelBench/ |
| ├── README.md # this file |
| ├── LICENSE # CC-BY-NC 4.0 full text |
| ├── ethics.md # surveillance consent + face anonymization rationale |
| ├── croissant.json # Croissant Core + RAI metadata |
| ├── data/ |
| │ ├── frames/ # 1,345 dirs × 8 .jpg = 10,760 jpg (~11 GB, anonymized) |
| │ ├── annotations/ # 1,345 canonical GT JSONs |
| │ ├── annotation_source.json # per-clip provenance map |
| │ └── safety_review/ # 186 safety officer reviews (parallel layer) |
| ├── manifests/ |
| │ ├── gt_clips.json # canonical 1,345 clip_id list |
| │ ├── batch_manifest.csv # per-clip metadata (site, work_area, BRISQUE, etc.) |
| │ ├── camera_zones.csv # zone tag per camera_id |
| │ └── safety_rules.yaml # rule definitions |
| ├── eval_data/ |
| │ ├── prompt_sensitivity_clips.json # 150-clip ablation subset |
| │ └── ablation_150_clips.json |
| └── sample/ # 50-clip stratified preview (594 MB) for reviewers |
| ├── clips/ # 50 .mp4 (full original clips for sanity-check) |
| ├── frames/ # 400 anonymized .jpg |
| ├── annotations/ # 47 canonical GT JSONs (3 of 50 lacked annotations |
| │ # in the canonical set; documented for transparency) |
| └── sample_manifest.csv |
| ``` |
|
|
| The `sample/` directory satisfies the NeurIPS Datasets & Benchmarks track |
| requirement that >4 GB datasets ship a small sample for reviewer inspection. |
|
|
| **Why no full mp4 clips in the main release?** The 8 representative jpgs per |
| clip were anonymized via face blurring; the full mp4s were not (re-encoding |
| 360+ frames per clip with face detection was out-of-scope for this release |
| and the camera-distance argument that justifies the low face-detection rate |
| on jpgs becomes less reliable across continuous video where movement reveals |
| more). The 50-clip `sample/` subdir does include mp4s for reviewer |
| inspection — this is a small, scoped exposure consistent with the |
| double-blind review process. For full mp4 access for legitimate research, |
| contact the authors after acceptance. |
|
|
| ## How to load |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import json |
| |
| # Full dataset |
| local_dir = snapshot_download(repo_id="steelbench/SteelBench", repo_type="dataset") |
| |
| # Just the manifest + annotations (no media) |
| local_dir = snapshot_download( |
| repo_id="steelbench/SteelBench", |
| repo_type="dataset", |
| allow_patterns=["manifests/*", "data/annotations/*", "README.md", "LICENSE"], |
| ) |
| |
| clip_ids = json.load(open(f"{local_dir}/manifests/gt_clips.json")) |
| ann = json.load(open(f"{local_dir}/data/annotations/{clip_ids[0]}.json")) |
| ``` |
|
|
| ## Datasheet for Datasets |
|
|
| ### Motivation |
|
|
| **For what purpose was the dataset created?** SteelBench was created to fill |
| a gap in VLM evaluation: existing video-and-action benchmarks (Kinetics, |
| ActivityNet, Charades) are curated, well-lit, and unambiguous; existing |
| industrial datasets (IndustryEQA, MonitorVLM, iSafetyBench) are simulated, |
| synthetic, or single-task. We needed a real-deployment benchmark with |
| multiple evaluation dimensions (perception, safety reasoning, calibration) |
| to test whether modern VLMs are deployment-ready in industrial monitoring. |
|
|
| **Who created the dataset?** The dataset was created by the SteelBench |
| authors as part of an academic research project. (Author identities |
| withheld during double-blind review.) |
|
|
| **Funding / interests:** No commercial relationship to the steel plant. |
| Footage shared under research-only data-use agreement. |
|
|
| ### Composition |
|
|
| **What does each instance represent?** A 15-second clip from a |
| fixed-position CCTV camera in a steel plant operational area. Each clip is |
| annotated with: scene-level action labels, per-person action codes (when |
| ≤5 workers), PPE assessment per worker, safety rule citations (when |
| violations are observed), spatial context tags, visibility conditions, |
| and an annotator-confidence score. |
|
|
| **How many instances are there?** 1,345 clips total. Per-site distribution |
| ranges from 1 (TAR Plant) to 211 (CRM 1&2). |
|
|
| **Does the dataset contain all possible instances or is it a sample?** |
| Sample. Source video totals ~149 hours from 117 unique videos; SteelBench |
| is a curated subset stratified for action-class balance and visibility |
| diversity. Curation pipeline is open-sourced in the companion code |
| repository (`extract_clips.py`, `filter_clips.py`, `curate_batch.py`). |
|
|
| **What data does each instance consist of?** |
| - A 15-second .mp4 clip (1080p, H.264) |
| - 8 evenly-spaced JPEG frames (anonymized — see `ethics.md`) |
| - A canonical GT annotation JSON with the structured fields documented in |
| `annotation_tool/schema_validator.py` |
|
|
| **Is there a label or target?** Yes — per-clip structured annotation with |
| multiple targets: action codes, PPE compliance, safety violations, scene |
| type, worker count, visibility conditions. |
|
|
| **Are there labeled subsets / splits?** |
| - `manifests/gt_clips.json` — full 1,345-clip benchmark |
| - `eval_data/prompt_sensitivity_clips.json` — 150-clip stratified ablation |
| subset (used in Section 7 prompt-sensitivity ablation in the paper) |
| - `eval_data/ablation_150_clips.json` — 150-clip stratified subset for |
| frame-density ablation |
| - `sample/` — 50-clip preview for review |
|
|
| **Are there missing modalities or relationships between instances?** |
| Different clips may share a source video and camera_id; this is |
| documented in `manifests/batch_manifest.csv`. |
|
|
| ### Collection process |
|
|
| **How was the data acquired?** CCTV footage from an operating integrated |
| steel plant, streamed continuously from 64 fixed cameras across 16 work |
| areas. Source videos cover December 2025–April 2026. |
|
|
| **What sampling/processing was applied?** |
| - Person detection (YOLOv8-n) on 0.5 fps sampled frames |
| - Detection-interval merging with 5 s gap tolerance and 2 s padding |
| - 15 s fixed-window slicing |
| - Quality filtering (BRISQUE, person-detection ratio, bounding-box area) |
| - Stratified curation by action class and visibility condition |
|
|
| **Who was involved in data collection?** Plant safety personnel installed |
| and maintain the camera infrastructure. The research team applied |
| processing and curation. The annotation pipeline involved 5 trained |
| tier-1 annotators, 2 domain experts (industrial safety), and 1 safety |
| officer. |
|
|
| ### Preprocessing / cleaning / labeling |
|
|
| **Was the data preprocessed/cleaned/labeled?** Yes. See the curation |
| pipeline above and the annotation tooling in |
| `annotation_tool/` (`schema_validator.py`, `safety_rules.py`, `app.py`). |
|
|
| **Was the "raw" data saved in addition to the preprocessed/cleaned/labeled |
| data?** Raw source videos are retained by the data provider but not |
| released; this dataset ships the curated 15 s clips only. |
|
|
| **Annotation methodology — model-assisted with audit:** |
| The annotation tool pre-fills the structured form using a single VLM |
| (Qwen3-VL-30B-A3B) so annotators verify rather than write from scratch. |
| The paper's audit protocol (Section 4) empirically bounds the influence |
| of this pre-fill via override rate, direction analysis, and dual-track |
| calibration (anchored vs blind). The full audit code and intermediate |
| audit data are in the companion code repository. |
|
|
| ### Uses |
|
|
| **For what purposes can the dataset be used?** |
| - Evaluation of VLMs on industrial action recognition, PPE detection, |
| and safety-violation reasoning |
| - Benchmarking calibration and degradation robustness |
| - Research on annotation provenance and audit protocols |
|
|
| **What restrictions apply?** |
| - Non-commercial use only (CC-BY-NC 4.0) |
| - No re-identification attempts |
| - No use for surveillance product training without separate agreement |
| - See `ethics.md` for full ethics statement |
|
|
| **Are there tasks for which the dataset should NOT be used?** |
| - Worker identification or biometric profiling (faces are blurred) |
| - Predictive surveillance of protected categories |
| - Production deployment without further validation (the paper shows no |
| evaluated VLM is deployment-ready: best DRS = 0.40) |
|
|
| ### Distribution |
|
|
| **Will the dataset be distributed?** Yes, on Hugging Face under CC-BY-NC 4.0. |
|
|
| **Is there an associated paper?** Yes, NeurIPS 2026 D&B submission. (Cite |
| once accepted; pre-acceptance citation withheld during review.) |
|
|
| ### Maintenance |
|
|
| **Who is supporting/hosting/maintaining the dataset?** The author team. |
| Issues and questions should be filed on the companion code repository. |
|
|
| **Are there errata?** Will be tracked as GitHub issues in the code repo |
| post-acceptance. |
|
|
| **Will old versions be supported/hosted?** Yes — Hugging Face dataset |
| versions are tagged. |
|
|
| ## Known limitations |
|
|
| - **Scene-level vs per-person**: Clips with >5 workers receive only |
| scene-level (Layer 1) annotations; per-person assessment (Layer 2) |
| applies to ≤5-worker scenes. |
| - **Anonymization is best-effort, not exhaustive** — see `ethics.md` for full method, and version history. |
|
|
| ## Anonymization (v1.1) |
|
|
| This is dataset version **1.1.0** (re-anonymized 2026-05-15). Two passes are applied: |
|
|
| 1. **Face blur.** |
| - JPGs (10,760 frames): **MediaPipe BlazeFace long-range** (`solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.3)`), 99×99 Gaussian + 20% pad. Detection rate 4.81%; low rate is structural at 7–10 m CCTV distance. |
| - Sample MP4s (50 clips, 13,714 frames): **OpenCV YuNet** (`face_detection_yunet_2023mar.onnx`, threshold 0.5), 99×99 Gaussian + 20% pad, applied per frame. Audio stripped on re-encode. |
|
|
| 2. **On-pixel text blur (new in v1.1).** EasyOCR (English + Hindi/Devanagari, threshold 0.20), 51×51 Gaussian + 8 px pad. Blurs text matching brand identifiers and ALL detected text in the top/bottom 80 px overlay bands. |
|
|
| - JPGs: 12,573 sensitive + 10,229 overlay regions blurred across 10,616 of 10,760 frames. |
| - Sample MP4s (every 5th frame): 3,347 sensitive + 2,363 overlay regions blurred. |
|
|
| See `data/anonymization_report.json` for the full machine-readable stats and `ethics.md` for the responsible-use statement. |
|
|
| ## Citation |
|
|
| ``` |
| @inproceedings{steelbench2026, |
| title = {SteelBench: Evaluating Vision-Language Models in Real-World |
| Industrial Environments}, |
| author = {Anonymous Authors}, |
| year = {2026}, |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under [Creative Commons Attribution-NonCommercial |
| 4.0 International (CC-BY-NC 4.0)](LICENSE). Code in the companion |
| repository is released under Apache-2.0. |
|
|
