This repository provides ready-to-use weights in PyTorch (.pt) and ONNX (.onnx) formats, a demo image, and the class label mapping for easy integration.

👉 For the full end-to-end system (APIs, web UI, training, evaluation, data tools), see the main project: https://github.com/yihong1120/Construction-Hazard-Detection

Labels

Index-to-name mapping used across all provided models (also in class_names.txt):

0: Hardhat
1: Mask
2: NO-Hardhat
3: NO-Mask
4: NO-Safety Vest
5: Person
6: Safety Cone
7: Safety Vest
8: Machinery
9: Utility Pole
10: Vehicle

Available models

Models are organized by model family (e.g., YOLO26 vs YOLO11).

YOLO26 (Recommended)

PyTorch (Ultralytics):
- models/yolo26/pt/yolo26n.pt
- models/yolo26/pt/yolo26s.pt
- models/yolo26/pt/yolo26m.pt
- models/yolo26/pt/yolo26l.pt
- models/yolo26/pt/yolo26x.pt
ONNX:
- models/yolo26/onnx/yolo26n.onnx
- models/yolo26/onnx/yolo26s.onnx
- models/yolo26/onnx/yolo26m.onnx
- models/yolo26/onnx/yolo26l.onnx
- models/yolo26/onnx/yolo26x.onnx

YOLO11 (Legacy)

PyTorch (Ultralytics):
- models/yolo11/pt/yolo11n.pt
- models/yolo11/pt/yolo11s.pt
- models/yolo11/pt/yolo11m.pt
- models/yolo11/pt/yolo11l.pt
- models/yolo11/pt/yolo11x.pt
ONNX:
- models/yolo11/onnx/yolo11n.onnx
- models/yolo11/onnx/yolo11s.onnx
- models/yolo11/onnx/yolo11m.onnx
- models/yolo11/onnx/yolo11l.onnx
- models/yolo11/onnx/yolo11x.onnx

Large binaries are tracked with Git LFS.

Quick start

A) Ultralytics (PyTorch)

python

from ultralytics import YOLO

# Load a model (choose the variant that fits your needs)
model = YOLO("models/yolo26/pt/yolo26x.pt")

# Inference on the demo image
results = model("data/examples/demo.jpg", imgsz=640, conf=0.25)

# Parse results (first image)
res = results[0]
boxes = res.boxes  # xyxy, confidence, class
for xyxy, conf, cls_id in zip(boxes.xyxy.tolist(), boxes.conf.tolist(), boxes.cls.tolist()):
    print(xyxy, conf, int(cls_id))

CLI option:

yolo predict model=models/yolo26/pt/yolo26x.pt source=data/examples/demo.jpg imgsz=640 conf=0.25

B) ONNX Runtime

python

import cv2
import numpy as np
import onnxruntime as ort

# Load and preprocess image to 640x640
img = cv2.imread("data/examples/demo.jpg")
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
size = 640
inp = cv2.resize(img_rgb, (size, size)).astype(np.float32) / 255.0
inp = np.transpose(inp, (2, 0, 1))[None, ...]  # 1x3x640x640

# Run ONNX model
session = ort.InferenceSession("models/yolo26/onnx/yolo26x.onnx", providers=["CPUExecutionProvider"])
input_name = session.get_inputs()[0].name
outputs = session.run(None, {input_name: inp})

pred = outputs[0]  # Typically (1, N, no)
print(pred.shape)

Post-processing (NMS, scaling back to original image) follows standard Ultralytics/YOLO routines.

File structure

.
├─ README.md
├─ LICENSE
├─ models/
│  ├─ yolo26/
│  │  ├─ pt/
│  │  │  ├─ yolo26n.pt
│  │  │  ├─ yolo26s.pt
│  │  │  ├─ yolo26m.pt
│  │  │  ├─ yolo26l.pt
│  │  │  └─ yolo26x.pt
│  │  └─ onnx/
│  │     ├─ yolo26n.onnx
│  │     ├─ yolo26s.onnx
│  │     ├─ yolo26m.onnx
│  │     ├─ yolo26l.onnx
│  │     └─ yolo26x.onnx
│  └─ yolo11/
│     ├─ pt/
│     │  ├─ yolo11n.pt
│     │  └─ ...
│     └─ onnx/
│        ├─ yolo11n.onnx
│        └─ ...
├─ data/
│  └─ examples/
│     └─ demo.jpg
└─ class_names.txt

Intended use and limitations

Intended for research and prototyping in construction safety monitoring.
Performance depends on camera viewpoint, lighting, occlusion, and domain gap.
For production, evaluate thoroughly on your target environment and consider rule-based filters and tracking.

Acknowledgements and sources

Main project and docs: https://github.com/yihong1120/Construction-Hazard-Detection
Dataset concept inspired by Roboflow construction safety datasets with extended annotations.
Roboflow dataset: https://app.roboflow.com/object-detection-qn97p/construction-hazard-detection
Models trained/exported using Ultralytics YOLO.

License

This repository is distributed under the AGPL-3.0 license. See LICENSE for details and ensure compliance, especially for networked deployments.

architchitte/Construction-Hazard-Detection

Construction-Hazard-Detection