yihong1120

YOLO11-based models for construction-site hazard detection. These models detect: - Workers without helmets and/or safety vests - Workers near machinery or vehicles - Workers in restricted areas (derived from safety cone clustering) - Machinery/vehicles near utility poles 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 Index-to-name mapping used across all provided models (also in `classnames.txt`): - PyTorch (Ultralytics): - `models/pt/bestyolo11n.pt` - `models/pt/bestyolo11s.pt` - `models/pt/bestyolo11m.pt` - `models/pt/bestyolo11l.pt` - `models/pt/bestyolo11x.pt` - ONNX: - `models/onnx/bestyolo11n.onnx` - `models/onnx/bestyolo11s.onnx` - `models/onnx/bestyolo11m.onnx` - `models/onnx/bestyolo11l.onnx` - `models/onnx/bestyolo11x.onnx` Post-processing (NMS, scaling back to original image) follows standard Ultralytics/YOLO routines. - 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. - 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. This repository is distributed under the AGPL-3.0 license. See `LICENSE` for details and ensure compliance, especially for networked deployments.