constructelligence/construction-site-safety-hazards

Construction Site Safety Hazards

PPE and heavy-equipment detection for construction-site imagery. A YOLOv8s detector that finds workers, worn and missing PPE — hard hats, hi-vis vests, gloves, goggles, safety boots — and heavy plant (excavators, wheel loaders, dump trucks) in a single pass. At 44.8 MB it runs entirely on-device, including in a web browser.

_{Predictions on held-out test images at confidence ≥ 0.4. Green: PPE worn · Red: PPE missing · Amber: heavy plant · Outline: worker.}

Test mAP@50	Classes	Input	Runs on
0.634 (v0.1: 0.442)	14	640 × 640	PyTorch · ONNX Runtime · Web

Intended use. Decision support for site teams: the model surfaces images and frames worth a closer look. It is not safety-rated, does not replace inspection by a competent person, and must not be used as a compliance record.

Model details

Version	v0.3
Architecture	YOLOv8s (Ultralytics), fine-tuned from COCO
Input	RGB 1×3×640×640, float32 in [0, 1], letterboxed with grey (114) padding
Output	1×18×anchors: cx, cy, w, h in input pixels, then one score per class (apply NMS)
Classes	person, hardhat, no-hardhat, safety vest, no-safety vest, no-mask, gloves, safety shoes, excavator, wheel loader, dump truck, goggles, no-goggles, no-gloves
Files	buildvision-hazards-v0.3.onnx · buildvision-hazards-v0.3.pt · predict_onnx.py · config.json · metrics.json
Licence	AGPL-3.0 (weights) · CC BY 4.0 (training data)

Performance

Evaluated on 573 held-out images from the test splits of 4 public construction-safety datasets, with near-duplicates of training and validation images removed.

mAP@50	mAP@50-95	Precision	Recall
0.634	0.373	0.754	0.604

By class

Class	Boxes	Precision	Recall	mAP@50	mAP@50-95	v0.1 mAP@50
person	713	0.874	0.892	0.908	0.555	0.807
hardhat	547	0.873	0.861	0.899	0.468	0.795
no-hardhat	126	0.452	0.381	0.379	0.143	0.047
safety vest	382	0.827	0.809	0.860	0.508	0.708
no-safety vest	222	0.635	0.581	0.522	0.241	0.368
no-mask — not detected	2	1.000	0.000	0.000	0.000	0.000
gloves	248	0.917	0.685	0.776	0.341	0.000
safety shoes	268	0.840	0.616	0.709	0.399	0.000
excavator	133	0.864	0.895	0.928	0.699	0.698
wheel loader	46	1.000	0.909	0.968	0.785	0.777
dump truck	78	0.849	0.821	0.888	0.712	0.666
goggles (new)	78	0.821	0.692	0.751	0.304	—
no-goggles (new)	67	0.384	0.254	0.208	0.045	—
no-gloves (new)	104	0.214	0.058	0.074	0.020	—

By source

Source	Images	mAP@50	mAP@50-95	v0.1 mAP@50
Construction Site Safety	34	0.531	0.337	0.503
PPE detection 1	101	0.846	0.505	0.641
PPE_Dectection v4	254	0.588	0.278	0.375
excavators-czvg9 (RF100)	184	0.911	0.695	0.677

Evaluation notes. Two sources label people incompletely; in their test images, people found by a stock COCO YOLOv8s (confidence ≥ 0.5) were added as person boxes so that correct detections are not counted as false positives. Scores reported by other PPE models use their own test sets and are not directly comparable.

Usage

Ultralytics

from ultralytics import YOLO

model = YOLO("buildvision-hazards-v0.3.pt")
for box in model("site.jpg", imgsz=640, conf=0.35)[0].boxes:
    print(model.names[int(box.cls)], float(box.conf), box.xyxy.tolist())

ONNX Runtime — no PyTorch required:

pip install onnxruntime numpy pillow
python predict_onnx.py site.jpg --out boxes.jpg

Browser — load the same ONNX file with onnxruntime-web (WASM). No server is involved; images never leave the device.

Training

• Data: Construction Site Safety, construction-safety-gsnvb (RF100), PPE detection 1, PPE_Dectection v4, excavators-czvg9 (RF100) — 7,450 training and 567 validation images, all CC BY 4.0.
• Recipe: YOLOv8s from COCO weights at 640 px. Mosaic and mixup augmentation with a cosine learning-rate schedule.
• Label clean-up: classes with only a handful of boxes across the corpus (barricade, dumpster, mask, mini-van, truck, safety net) were removed before training.

Limitations

• Missing PPE is harder than worn PPE. no-hardhat (0.379), no-goggles (0.208), no-gloves (0.074) trail their worn counterparts; expect missed violations and some false alarms, especially on unfamiliar sites and cameras.
• Unsupported output channels. no-mask — too few training examples to learn; ignore these outputs.
• No fall protection. Harnesses, lanyards and edge protection are not labelled in any training source.
• No zones. Exclusion zones around plant and vehicles are not predicted; derive them downstream from the detected boxes.
• Model size. YOLOv8s is chosen to run on-device; larger models trained on the same data did not score higher.

FAQ

Which PPE does it detect? Hard hats, hi-vis vests, gloves, goggles and safety boots — and, for hard hats, vests, gloves and goggles, their absence.

Which equipment? Excavators, wheel loaders and dump trucks, plus workers (person).

Does it run offline? Yes. The ONNX export runs in ONNX Runtime on CPU or in the browser with no network access.

Can it be used for compliance decisions? No. It is a screening aid for people, not a safety system.

Licence

• Weights — AGPL-3.0. Trained with Ultralytics YOLOv8, whose models and derivatives are AGPL-3.0 unless covered by an Ultralytics Enterprise licence. Offering the model in a network service requires publishing that service's source.
• Data — CC BY 4.0. Sources below.

Attribution

• Construction Site Safety — Roboflow Universe Projects, CC BY 4.0 — https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety
• construction-safety-gsnvb (RF100) — Roboflow 100, CC BY 4.0 — https://universe.roboflow.com/roboflow-100/construction-safety-gsnvb
• PPE detection 1 — vincentspace, CC BY 4.0 — https://universe.roboflow.com/vincentspace/ppe-detection-1-cniwr
• PPE_Dectection v4 — himanshu-bharati, CC BY 4.0 — https://universe.roboflow.com/himanshu-bharati/ppe_dectection-dtt4q
• excavators-czvg9 (RF100) — Roboflow 100, CC BY 4.0 — https://universe.roboflow.com/roboflow-100/excavators-czvg9

Person pseudo-labels in the test set come from Ultralytics YOLOv8s (COCO).

Citation

@misc{construction-site-safety-hazards,
  title  = {Construction Site Safety Hazards: PPE and Heavy-Equipment Detection},
  author = {Constructelligence},
  year   = {2026},
  url    = {https://huggingface.co/constructelligence/construction-site-safety-hazards}
}