Recycling Waste Classification Using Vision Transformer on Portable Device

Huang, Kai; Lei, Huan Yao; Jiao, Zeyu; Zhong, Zhenyu

doi:10.3390/su132111572

Cited by 24 publications

(15 citation statements)

References 33 publications

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“…1) Measurement Principle: The waste category classification approaches are categorized into contact-based, i.e., those having active contact with target objects [102], [108], and nocontact-based approaches [116], [117], [118], [119], [120], [121]. Furthermore, deep learning (DL)-based algorithms employing RGB and RGB-depth (RGBD) sensors have been used to detect and segment individual waste items from a densely cluttered pile [4], [78], [109], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136].…”

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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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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“…For a comparison experiment classifying a collection of rubbish, the two top-performing models were chosen and installed separately onto the automatic sorting machine. Huang et al, [8] suggested to increase the accuracy of automatic classification using a deep neural network architecture called Vision Transformer that is only dependent on self-attention mechanisms. The suggested method may obtain the highest accuracy of 96.98%, which is superior to the current CNN-based method, according to experimental results on the TrashNet dataset.…”

Section: Related Workmentioning

confidence: 99%

Iweews: An Intelligent Waste Extractor for Efficient Waste Segregation by Using Deep Learning Technique

Bobde M,

Jothi,

Sharma

2023

IJEAST

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Waste segregation is a top priority in many countries across the world. Even in this day and age, many individuals are unable to discern between biodegradable, recyclable, and landfill waste. As a result, the world is currently dealing with a serious basic issue with garbage disposal. In this study, we use deep learning algorithms to handle the trash categorization problem. Organic waste, recyclable rubbish, and landfill garbage are the three major types of waste. A way to speed up the whole process is by doing waste sorting, which could be done by computer using image recognition. CNNs have gradually been used in a number of intelligent trash identification and recycling applications during the past few years (IWIR). Nevertheless, because there aren't any established standards or benchmarks for the datasets and models being utilised, current studies on IWIR are difficult to summarise because CNNs are still relatively new to environmental researchers.EfficientNet-B3 CV model which could be utilized in this scenario due to the more efficient architecture and comparable performance with others deep convolutional neural network. Deep learning has now made it possible to overcome one of the biggest obstacles to effective waste management and hence, this issue can be handled with utmost care.

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“…Manual labeling of plastic in the image is a work-intensive task. However, labelers have done their best to identify only plastic though there will be some unavoidable errors in the labeling due to difficulty in perceiving the material [69]. Plastic litter is the bulk of the litter in the marine environment and the greatest threat to marine ecosystems.…”

Section: Dataset Preparationmentioning

confidence: 99%

Detection of River Plastic Using UAV Sensor Data and Deep Learning

et al. 2022

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Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

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Recycling Waste Classification Using Vision Transformer on Portable Device

Cited by 24 publications

References 33 publications

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

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

Iweews: An Intelligent Waste Extractor for Efficient Waste Segregation by Using Deep Learning Technique

Detection of River Plastic Using UAV Sensor Data and Deep Learning

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