YOLOv5 on the Construction Site: Helmets, Heads, and Danger Zones

What it does Smart_Construction is a YOLOv5 v2.x application tuned for construction-site safety. It detects three classes—person, head, and helmet—to identify workers missing hard hats, and it can highlight people who enter user-marked danger zones. A PyQt5 visualization tool, added in March 2021, gives operators a point-and-click interface instead of a terminal.

The interesting bit The project’s value is in the plumbing, not the architecture. The author provides pre-trained weights for three model sizes and a merge script that augments a head-only helmet dataset with full-body person labels by running a separate detection pass and combining the results.

Key highlights

• Pre-trained weights for YOLOv5s/m/l with overall mAP@0.5 scores ranging from 0.888 to 0.906
• PyQt5-based visualization interface for running detection without command-line arguments
• Dangerous-area mode that draws red zones and only flags person detections inside them
• ONNX and TorchScript export support via the upstream export script
• Dataset utilities to merge third-party person labels into the base Safety-Helmet-Wearing-Dataset

Caveats

• Explicitly built on YOLOv5 v2.x, an older release branch that does not include subsequent upstream improvements
• Pre-trained weights are distributed exclusively through Baidu Cloud links with Chinese-language extraction codes
• The README prominently advertises paid remote assistance for graduation and course designs, signaling a student-project audience

Verdict Worth a download if you need a Chinese-language reference for construction-site detection or a quick academic prototype. Skip it if you want a current YOLO release with internationally hosted weights.