Deploying U²-Net for Intelligent Background Removal

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NVIDIA AIBOX Series

Both the AIBOX-OrinNano and AIBOX-OrinNX are equipped with original NVIDIA Jetson Orin core modules. They come standard with an industrial-grade all-metal enclosure featuring an aluminum alloy structure for thermal conduction. The top cover utilizes a slatted grille design on the sides for highly efficient heat dissipation, ensuring computational performance and stability under high-temperature operation to meet the demands of various industrial applications.

Background Removal

Background Removal technology has become an essential tool in the field of image processing, primarily applied in scenarios such as image editing, data analysis, and application development.

Typical Applications:

(1) Image Processing: E-commerce product cutouts, portrait retouching, and medical image analysis.

(2) Video Processing: Real-time green screen replacement and dynamic object tracking.

(3) Scientific Preprocessing: Enhancing quantification accuracy in meteorological chromatography analysis through background removal.

U²-Net

U²-Net (U-squared Net) is a deep learning-based image segmentation model specifically designed for high-precision background removal tasks. Its core technical features and application scenarios include:

• Dual U-shaped Encoder-Decoder Structure
• Deep Supervision and Loss Functions
• Lightweight Design

Download Source Code

$ git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference

Build / Install

Reference: https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md

Run Examples

# Remove the background (with alpha)
$ ./backgroundnet.py images/bird_0.jpg images/test/bird_mask.png                   

# Replace the background
$ ./backgroundnet.py --replace=images/snow.jpg images/bird_0.jpg images/test/bird_replace.jpg