Object Detection

Overview

Object Detection node detects objects in an image using a custom-loaded model and a configurable post-processing pipeline.

This node gives full control over the detection stack: model selection, confidence filtering, NMS, label mapping, frame tiling, and overlay styling. Use this node when General Object Detection does not offer enough control, or when you need to run a domain-specific model.

Input

Input Image

image required

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

Model Directory Path

string required

Path to the model directory to load for inference. See Model Directory Path for details.

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: 0.3

NMS Threshold

number required advanced

Non-Maximum Suppression (NMS) threshold used to remove overlapping detections. See NMS Threshold for tuning guidance.

Range: 0.0 to 1.0
Default: 0.5

Overlay Results

boolean required advanced

Whether to draw detection overlays on output frame. See Overlay Results.

Default: true

Advanced Inputs

The node also supports advanced options for:

• TensorRT backend (useTrtIfAvailable)
• Frame tiling (divideFrame and legacy splitFrame)
• Label transformations (transformContains, transformLabels)
• Label filtering (sKeepLabels, sIgnoreLabels)
• Label color mapping (labelColors)
• Framework/architecture-specific settings for non-.nam models (see Advanced Setting)

Output

Overlay Image

image

Frame with detection overlays.

Detected Count

integer

Number of detected objects.

Detected Objects

array

Array of detected objects, each containing:

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

Notes

• Use a higher confidence threshold to reduce false positives.
• Tune NMS threshold based on how close objects appear in your scene.
• When objects are small in high-resolution frames, frame division can improve detection recall.