# Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs

arXiv: https://arxiv.org/abs/1802.08960 · HTML: https://mlsanity.com/paper/1802.08960/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [Frequency-aware Feature Fusion for Dense Image Prediction](https://mlsanity.com/paper/2408.12879/) (2024)
- [Open-World Semantic Segmentation Including Class Similarity](https://mlsanity.com/paper/2403.07532/) (2024)
- [Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data](https://mlsanity.com/paper/2212.03690/) (2022)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [When Semantic Segmentation Meets Frequency Aliasing](https://mlsanity.com/paper/2403.09065/) (2024)
- [WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2312.09534/) (2023)
- [PrevMatch: Revisiting and Maximizing Temporal Knowledge in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2405.20610/) (2024)
- [Spatial Frequency Modulation for Semantic Segmentation](https://mlsanity.com/paper/2507.11893/) (2025)
- [Deep Learning Techniques for In-Crop Weed Identification: A Review](https://mlsanity.com/paper/2103.14872/) (2021)
- [Open-World Panoptic Segmentation](https://mlsanity.com/paper/2412.12740/) (2024)
- [CaRaFFusion: Improving 2D Semantic Segmentation with Camera-Radar Point Cloud Fusion and Zero-Shot Image Inpainting](https://mlsanity.com/paper/2505.03679/) (2025)
- [WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2403.14874/) (2024)
- [Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming](https://mlsanity.com/paper/2407.14119/) (2024)
- [Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation](https://mlsanity.com/paper/2406.05837/) (2024)
- [Leveraging Out-of-Distribution Unlabeled Images: Semi-Supervised Semantic Segmentation with an Open-Vocabulary Model](https://mlsanity.com/paper/2507.03302/) (2025)

## Abstract (arXiv)

The ability to interpret a scene is an important capability for a robot that is supposed to interact with its environment. The knowledge of what is in front of the robot is, for example, relevant for navigation, manipulation, or planning. Semantic segmentation labels each pixel of an image with a class label and thus provides a detailed semantic annotation of the surroundings to the robot. Convolutional neural networks (CNNs) are popular methods for addressing this type of problem. The available software for training and the integration of CNNs for real robots, however, is quite fragmented and often difficult to use for non-experts, despite the availability of several high-quality open-source frameworks for neural network implementation and training. In this paper, we propose a tool called Bonnet, which addresses this fragmentation problem by building a higher abstraction that is specific for the semantic segmentation task. It provides a modular approach to simplify the training of a semantic segmentation CNN independently of the used dataset and the intended task. Furthermore, we also address the deployment on a real robotic platform. Thus, we do not propose a new CNN approach in this paper. Instead, we provide a stable and easy-to-use tool to make this technology more approachable in the context of autonomous systems. In this sense, we aim at closing a gap between computer vision research and its use in robotics research. We provide an open-source codebase for training and deployment. The training interface is implemented in Python using TensorFlow and the deployment interface provides a C++ library that can be easily integrated in an existing robotics codebase, a ROS node, and two standalone applications for label prediction in images and videos.