PPE-Detection (Personal Protective Equipment Detection) is a computer vision-based technology designed to automatically identify whether personnel are wearing essential safety gear, such as helmets, reflective vests, goggles, masks, and gloves. Using deep learning algorithms (e.g., YOLO, Faster R-CNN), this technology enables real-time detection and classification of safety equipment in high-risk environments like construction sites, factories, and healthcare facilities, significantly reducing occupational hazards. The system analyzes data from cameras or drones, integrating object detection and semantic segmentation to pinpoint non-compliant behaviors and trigger immediate alerts. Key challenges include handling occlusions in complex scenarios, multi-scale object recognition, and optimizing cross-device deployment. With advancements in edge computing and lightweight models, PPE-Detection is evolving toward cost-effective, intelligent safety management solutions, enhancing compliance and operational safety standards globally.

Source model

• Input shape: 1x3x320x192
• Number of parameters: 5.92M
• Model size: 23.64M
• Output shape: [1x21x40x24],[1x21x20x12],[1x21x10x6]

The source model can be found here

When the user has fine-tuned the source model, the model conversion process must be performed again.

Users can refer to either of the following two methods to complete the model conversion:

• Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.

• Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.

The model performance benchmarks and example code provided by Model Farm are all implemented based on the APLUX AidLite SDK.

For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:

Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation

Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation

Inference Example Code

The inference example code is implemented using the AidLite SDK.

Click Model & Code to download the model files and the inference code package. The file structure is as follows:

/model_farm_{model_name}_aidlite
    
    |__ models # folder where model files are stored

    |__ python # aidlite python model inference example

    |__ cpp # aidlite cpp model inference example

    |__ README.md