WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks
- URL: http://arxiv.org/abs/2409.16999v1
- Date: Wed, 25 Sep 2024 15:04:21 GMT
- Title: WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks
- Authors: Alberto Bacchin, Leonardo Barcellona, Matteo Terreran, Stefano Ghidoni, Emanuele Menegatti, Takuya Kiyokawa,
- Abstract summary: We introduce a data augmentation method based on a novel GAN architecture called wasteGAN.
The proposed method allows to increase the performance of semantic segmentation models, starting from a very limited bunch of labeled examples.
We then leverage the higher-quality segmentation masks predicted from models trained on the wasteGAN synthetic data to compute semantic-aware grasp poses.
- Score: 7.775894876221921
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and labeling limits its applicability in real-world scenarios like waste sorting. To tackle this issue, we introduce a data augmentation method based on a novel GAN architecture called wasteGAN. The proposed method allows to increase the performance of semantic segmentation models, starting from a very limited bunch of labeled examples, such as few as 100. The key innovations of wasteGAN include a novel loss function, a novel activation function, and a larger generator block. Overall, such innovations helps the network to learn from limited number of examples and synthesize data that better mirrors real-world distributions. We then leverage the higher-quality segmentation masks predicted from models trained on the wasteGAN synthetic data to compute semantic-aware grasp poses, enabling a robotic arm to effectively recognizing contaminants and separating waste in a real-world scenario. Through comprehensive evaluation encompassing dataset-based assessments and real-world experiments, our methodology demonstrated promising potential for robotic waste sorting, yielding performance gains of up to 5.8\% in picking contaminants. The project page is available at https://github.com/bach05/wasteGAN.git
Related papers
- Advancing Recycling Efficiency: A Comparative Analysis of Deep Learning Models in Waste Classification [0.0]
The research tackles the pressing issue of waste classification for recycling by analyzing various deep learning models.
The results indicate the method significantly boosts accuracy in complex waste categories.
The research paves the way for future advancements in multi-category waste recycling.
arXiv Detail & Related papers (2024-11-05T03:44:54Z) - SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation [46.178512739789426]
We present SpectralWaste, the first dataset collected from an operational plastic waste sorting facility.
This dataset contains labels for several categories of objects that commonly appear in sorting plants.
We propose a pipeline employing different object segmentation architectures and evaluate the alternatives on our dataset.
arXiv Detail & Related papers (2024-03-26T18:39:38Z) - Bridging the Gap: Enhancing the Utility of Synthetic Data via
Post-Processing Techniques [7.967995669387532]
generative models have emerged as a promising solution for generating synthetic datasets that can replace or augment real-world data.
We propose three novel post-processing techniques to improve the quality and diversity of the synthetic dataset.
Experiments show that Gap Filler (GaFi) effectively reduces the gap with real-accuracy scores to an error of 2.03%, 1.78%, and 3.99% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, respectively.
arXiv Detail & Related papers (2023-05-17T10:50:38Z) - VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting [61.52419223232737]
In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream.
We present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
arXiv Detail & Related papers (2023-03-26T21:38:38Z) - Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor
Point Clouds [69.64240235315864]
This paper introduces the synthetic-to-real domain generalization setting to this task.
The domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns.
Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap.
arXiv Detail & Related papers (2022-12-09T05:07:43Z) - Convolutional generative adversarial imputation networks for
spatio-temporal missing data in storm surge simulations [86.5302150777089]
Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods.
We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
arXiv Detail & Related papers (2021-11-03T03:50:48Z) - Towards artificially intelligent recycling Improving image processing
for waste classification [0.0]
IBM's Wastenet project aims to improve recycling by using artificial intelligence for waste classification.
This paper builds on this project through the use of transfer learning and data augmentation techniques.
Results show that these augmentation techniques further improve the test accuracy of the final model to 95.40%.
arXiv Detail & Related papers (2021-08-09T21:41:48Z) - ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste.
This dataset contains over1800fully segmented video frames collected from a real waste sorting plant.
We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
arXiv Detail & Related papers (2021-06-04T22:17:09Z) - Bridging the Gap Between Clean Data Training and Real-World Inference
for Spoken Language Understanding [76.89426311082927]
Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference.
We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space.
Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
arXiv Detail & Related papers (2021-04-13T17:54:33Z) - Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision
Farming [3.4788711710826083]
We propose an alternative solution with respect to the common data augmentation methods, applying it to the problem of crop/weed segmentation in precision farming.
We create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts.
In addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images.
arXiv Detail & Related papers (2020-09-12T08:49:36Z) - Learning to Count in the Crowd from Limited Labeled Data [109.2954525909007]
We focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples.
Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data.
arXiv Detail & Related papers (2020-07-07T04:17:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.