This repository implements the uNET architecture for detecting boxes, designed specifically for a robot-based bin-picking system. The project focuses on the computer vision aspect, particularly on identifying boxes within a given scene. The robust implementation of the uNET architecture facilitates precise box detection, enabling seamless integration with robotic systems for efficient bin picking tasks.
Utilizes uNET architecture for accurate box detection. Specifically tailored for robot-based bin-picking systems. Designed to enhance automation and efficiency in industrial settings. Usage:
Ideal for developers and engineers working on robotic systems. Provides a foundation for implementing box detection in industrial environments.
Contributions: Contributions are welcome to enhance the efficiency and versatility of box detection algorithms, enabling broader applications in industrial automation.
You can download a pre-trained model for semantic segmentation from here. Place the model into the models/ directory.
For convenience, you can run the inference on Google Colab using our pre-trained models. Access the Colab notebook here. You can see some example inference images from here.
We also trained the model using instant segmentation, but we couldn't get satisfactory outputs from that. You can access the pre-trained model here. The new dataset used for instant segmentation is available here.
On macOS, .DS_Store files are automatically created by Finder to store folder view settings. In this project, it’s important to handle these files properly.