Safety Helmet Detector
Construction sites are dangerous places where accidents can happen at any time. Therefore, it is important to keep workers safe by enforcing safety measures, such as helmet use. However, monitoring worker compliance can be a challenging task. This is where a detection model can come in handy.
In this tutorial, you will build a detection model that can detect whether or not workers on a construction site are wearing helmets.
To create the model, use the publicly available Safety Helmet Detection dataset, which contains 5000 images with bounding box annotations for 3 classes: helmet, person, and head.
Adding the dataset
Go to the
Owned datasets and click
Add new dataset. Enter a dataset name, select
Personal dataset type, and click
The Safety Helmet Detection dataset has been saved in a .zip archive consisting of two folders: annotations and images. You can upload it either as an archive or as a directory. For the purpose of this tutorial, we will use the second option. Click
Wait for the site to validate the uploaded directory.
In the thumbnails, you will see the images with added annotations. Once the directory is loaded, click
Before uploading, you will be informed of our image size limits. Decide what the application should do if the size limit is exceeded. For the purposes of this tutorial select
The uploading task is performed in the background. You can monitor the progress in the
Creating the model
Go to the
Models section and click
Add new model. Select Detection.
From the Safety Helmet dataset select helmet and head categories.
Skip the merge categories step and click
Next. Enter the name of your model and select the
Configure manually option. Click
Change the pretrained model to
yolo4-tiny.conv.29 and click
Start training. You can view the training progress in the
Dashboard view, in the
Notifications tab, or in the
Testing your idea
Once the training is complete, go to the
Click on the Safety Helmet Detector model to run it.
Select the NVIDIA MAXWELL architecture. Click the hardware tile to convert.
Once converted, click on the device to connect to it.
Copy the registration code to the clipboard, then click
Copy token and go to device. This will open a new tab in your browser, where you will find a web app that is now using your local Nano device.
Enter your e-mail, paste your registration code into the
Token field, and create a password. Once registered, the device will change its status to Connected in the
Live Testing section of the OSAI app.
Click on the model tile to download it to the device's local memory. Click on the model again and select the input data required to test the model. Click the
Upload File button. You can upload either a photo or a video. For the purpose of this tutorial, we will use two different video files.
Once your video file has been uploaded, you can change its settings and decide whether or not to create a new dataset with the uploaded videos. Click
Next to continue.
After the video is processed by the web app, you can view the results.
Keep in mind You can always improve the effectiveness of the trained model by subjecting it to a re-training process.