Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste

Patrizi, Arianna; Gambosi, Giorgio; Zanzotto, Fabio Massimo

doi:10.3390/jimaging7080144

Cited by 11 publications

(9 citation statements)

References 26 publications

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“…These problems increase the cost of building a dataset and delay training based on new data. Some studies have successfully attempted to overcome this hurdle by utilizing synthetic data generation 39 . But synthetic images are inherently different from actual images 40 .…”

Section: Methodsmentioning

confidence: 99%

A pixel-level coarse-to-fine image segmentation labelling algorithm

Lee

Ilyas

Jin

et al. 2022

Sci Rep

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Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.

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Section: Methodsmentioning

confidence: 99%

A pixel-level coarse-to-fine image segmentation labelling algorithm

Lee

Ilyas

Jin

et al. 2022

Sci Rep

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“…One approach includes data augmentation to enrich image datasets [151], [152], [153], and the other addresses the simplification of labor-intensive annotation processes [154], [155], [156], [157], [158], [159]. Na et al [160] and Patrizi et al [161] have tried to augment the dataset for waste recognition.…”

Section: B Sensors and Recognitionmentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners-Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide Manuscript

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“…Recent publications 28,29 demonstrated the interest of data augmentation for improving the generalization capability of CNN trained on synthethic images.…”

Section: Case Studymentioning

confidence: 99%

Investigating sanitized and controlled image dataset to train deep convolutional neural networks for remote object detection on the field

Pichler

Hueber

Hennequin

2022

Electro-Optical Remote Sensing XVI

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Performing specific object detection and recognition at the imaging sensor level, raises many technical and scientific challenges. Today state-of-the-art detection performances are obtained with Deep Convolutional Neural Network (CNN) models. However reaching the expected CNN behavior in terms of sensitivity and specificity require to master the training dataset. We explore in this paper, a new way of acquiring images of military vehicles in sanitized and controlled conditions of the laboratory in order to train a CNN to recognize the same visual signature with real vehicles in realistic outdoor situations. By combining sanitized images, counter-examples and different data augmentation techniques, our investigations aim at reducing the needs of complex outdoor image acquisition. First results demonstrate the feasibility to detect and classify, in real situations, military vehicles by exploiting only signatures from miniature models.

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Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste

Cited by 11 publications

References 26 publications

A pixel-level coarse-to-fine image segmentation labelling algorithm

A pixel-level coarse-to-fine image segmentation labelling algorithm

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Investigating sanitized and controlled image dataset to train deep convolutional neural networks for remote object detection on the field

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