General Object Detection

Overview

General Object Detection node provides ready-to-use object detection with a built-in model and class filtering.

Compared with Object Detection, this node is optimized for quick setup: pick a model size, list the labels you care about, and tune confidence/NMS only if needed. No external model files are required.

Input

Input Image

image required

The image frame to analyze. Connect this to a camera or upstream image output.

Model Size

string required

Size of the built-in detection model. Larger models are more accurate but slower.

Values:

• TINY — fastest; suited for high-frame-rate flows or resource-limited hardware.
• SMALL — balanced speed and accuracy.
• MEDIUM (default) — most accurate of the three; recommended unless speed is a concern.

Detecting Labels

array required

List of COCO class label strings to keep in the output. Detections for labels not in this list are discarded. Use this to focus results on the object types relevant to your flow.

Default: person, car

Confidence Threshold

number required advanced

Minimum confidence score to keep a detection. See Confidence Threshold for tuning guidance.

Range: 0.0 to 1.0
Default: 0.5

NMS Threshold

number required advanced

Non-Maximum Suppression threshold to suppress overlapping boxes. See NMS Threshold for tuning guidance.

Range: 0.0 to 1.0
Default: 0.3

Overlay Results

boolean required advanced

Whether to draw detections on output frame. See Overlay Results.

Default: true

Use TensorRT If Available

boolean advanced

Enable TensorRT inference backend when available. Can significantly improve throughput on NVIDIA GPUs.

Default: false

Output

Overlay Image

image

Frame with detected object overlays.

Detected Count

integer

Number of detected objects.

Detected Objects

array

Array of detection objects with:

• bbox array: [x, y, width, height]
• label string
• confidence number