安全帽正确佩戴数据集 Object Detection Dataset and Pre-Trained Model by SRT

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Explore Dataset

Here are a few use cases for this project:

Workplace Safety Monitoring: The model can be used in construction sites, factories, or any industrial working environments where wearing a helmet is compulsory. The system can alert supervisors if any worker is identified without a helmet or wearing it incorrectly, ensuring the enforcement of safety rules.
Road Traffic Enforcement: This AI system can be effective in enforcing traffic safety rules among motorcyclists. Traffic cameras equipped with this model can identify and flag riders not wearing a helmet or wearing it improperly, assisting law enforcement agencies significantly.
Personal Protective Equipment (PPE) Training: This model could be integrated into training programs for professionals who need to wear safety helmets. It could provide real-time feedback on whether the trainee is wearing the helmet correctly, enhancing the effectiveness of training sessions.
Helmet Manufacturing: This system can be used in the manufacturing process to identify any potential design faults before the helmets come to market. In addition, it can be used in quality control to ensure that the helmets manufactured align with the safety regulations and standards.
Sports Safety Compliance: In sporting activities like cricket, baseball, horse riding, or motorcycle racing where helmets are compulsory, this model can be leveraged to ensure the athletes' safety by continually monitoring correct helmet usage during the game or race.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            -subtn_dataset,
                            title = { 安全帽正确佩戴数据集 Dataset },
                            type = { Open Source Dataset },
                            author = { SRT },
                            howpublished = { \url{ https://universe.roboflow.com/srt-ckenx/-subtn } },
                            url = { https://universe.roboflow.com/srt-ckenx/-subtn },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-03 },
                            }