Safety Equipment Detection

This function block checks for common safety equipments on an input image and returns an annotated image plus per-class counts. It is intended for visual inspection workflows where helmets, vests, goggles and gloves must be detected and counted.

📥 Inputs

Image Provide the image you want analyzed (single image frames, stream frames, or loaded images).

📤 Outputs

Output Image Annotated image with detections drawn for visualization. Helmet Count Number of detected helmets. Safety Vest Count Number of detected safety vests. Safety Goggle Count Number of detected safety goggles. Safety Glove Count Number of detected safety gloves. No Helmet Count Number of detected people without helmets. No Safety Vest Count Number of detected people without vests. No Safety Goggle Count Number of detected people without goggles. No Safety Glove Count Number of detected people without gloves.

🕹️ Controls

Confidence Ratio Adjust detection confidence threshold. Higher values make detections stricter (fewer false positives); lower values increase sensitivity (more detections, possibly more false positives).

Tip: start around 0.7–0.9 and fine-tune based on your scene.

🎯 Features

• Real-time visual feedback with annotated detections on Output Image.

• Per-class counting for both presence and absence of required safety items.

• Adjustable detection confidence using Confidence Ratio to suit varying lighting and scene conditions.

• Designed to work with live camera frames or pre-recorded images.

⚙️ How it runs

When an image is provided on the Image input, the block analyzes the image, marks detected safety items on a visual output image and returns numeric counts for each class. The block may require a short loading time on first run (model preparation), after which it processes images continuously as they arrive.

📝 Usage Instructions

1. Provide an image source to the Image input (live camera feed or loaded image).

2. Adjust Confidence Ratio to suit your scene (lighting, scale, occlusions).

3. Use the annotated Output Image to visually confirm detections and read numeric outputs for automation or logging.

💡 Tips and Tricks

• Combine with live camera inputs for continuous monitoring: e.g., Camera USB, Camera IP, or Stream Reader to feed frames into this block.

• To visualize and inspect frames interactively, connect the visual output to Show Image and use the See Image viewer.

• Improve detection focus and reduce false positives by cropping to the area of interest using Image ROI Select before feeding images to this block.

• Speed up processing when full resolution is unnecessary by inserting Image Resizer before this block.

• Overlay or emphasize detection boxes using Draw Detections for clearer on-screen presentation.

• For multi-frame workflows, combine with tracking: feed detection outputs (rectangles / classes) into Object_Detection_Tracker to maintain IDs over time and count unique persons.

• Log and export results: use Image Logger or Image Write to save frames, and CSV Export or Data to JSON to store counts. For real-time alerts integrate with MQTT Publish or Send Mail for notifications.

• For crowd or distancing analysis, use together with Social Distance Detector or Pose Estimation to correlate PPE usage with person location or posture.

🛠️ Troubleshooting

• No detections or too many false positives: adjust Confidence Ratio and re-evaluate. Try values between 0.6 and 0.9.

• Poor results in low-light or low-contrast scenes: improve lighting, use Contrast Optimization or Denoising prior to this block.

• Detections outside the area of interest: add Image ROI Select to limit the search area.

• Slow performance: reduce input image size with Image Resizer or lower frame rate upstream. Performance also depends on available hardware.

• If the block is not ready on first use, allow a short time for model preparation (loading) before expecting outputs.