Object Detection (D-FINE)

This function block performs real-time object detection on an input image. It lets you choose a model size (trade-off between speed and accuracy), filter which object classes to detect, set a confidence threshold, and optionally draw color-coded bounding boxes on the output image.

📥 Inputs

Image The image to analyze for object detections.

📤 Outputs

Result Annotated image with bounding boxes and labels (present if drawing is enabled).

Boxes A list of bounding box coordinates for each detection (format: [x1, y1, x2, y2]).

Labels Class names for each detected object.

Scores Confidence score for each detection.

🕹️ Controls

Model Choose model size (examples: Nano / Small / Medium / Large / XLarge) to balance inference speed vs accuracy.

Select Classes Table to tick only the COCO classes you want the block to report (leave all unchecked to allow all classes).

Draw Boxes Toggle to enable/disable drawing colored bounding boxes and labels on the output image.

Threshold Slider to set the confidence score threshold (0–100) for accepting detections.

🎨 Features

• Model size selection to prioritize speed or accuracy.

• Class filtering so you only get detections you care about.

• Confidence threshold control to reduce false positives.

• Optional visual output with color-coded bounding boxes and readable labels.

• Returns both visual (image) and structured detection data (boxes, labels, scores) for downstream processing.

📝 Usage Instructions

1. Connect an image-producing block to the Image input.

2. Choose a Model size based on whether you need faster results or higher accuracy.

3. Use Select Classes to limit detection to only the object types you need (or leave empty to accept all).

4. Adjust the Threshold slider to tune detection sensitivity.

5. Enable Draw Boxes if you want a visual result returned on the Result output.

6. Use the Boxes, Labels, and Scores outputs in subsequent processing or logging blocks.

📊 Evaluation

When run, this block processes the incoming image according to the chosen Model and Threshold and outputs detections. If Draw Boxes is enabled, an annotated image will be available on Result; detection metadata is always available on the other outputs.

💡 Tips and Tricks

• If small objects are missed, try feeding a higher-detail input using Super Resolution or avoid downscaling the input with Image Resize.

• To reduce processing time, pick a smaller Model (Nano/Small) at the cost of some accuracy.

• Crop the area of interest before detection with Image ROI Select or Image ROI to improve speed and reduce false positives.

• For visual debugging, connect the Result output to Show Image so you can inspect detections interactively.

• If you need to keep detections across frames (tracking), combine outputs with Object_Detection_Tracker.

• Save frames with detections using Image Logger, Image Write, or Record Video for later review.

• Export detection metadata (boxes / labels / scores) with CSV Export or Data to JSON for analytics and reporting.

• When experimenting, monitor system usage with GPU Statistics to choose an appropriate Model size for your hardware.

• If you want different detection behavior or to try other models, compare outputs with Object Detection - Custom or other object detection alternatives.

🛠️ Troubleshooting

• If you see no detections, lower the Threshold or make sure the desired class is checked in Select Classes.

• If detections are noisy, raise the Threshold or limit classes in Select Classes.

• If processing is too slow, choose a smaller Model or crop the input image using Image ROI Select.

• If the output image is empty while other outputs contain data, ensure Draw Boxes is enabled to get a visual result.