Valuable Waste Classification Modeling based on SSD-MobileNet

Thokrairak, Sorawit; Thibuy, Kittiya; Jitngernmadan, Prajaks

doi:10.1109/incit50588.2020.9310928

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…The SSD series has been adopted in waste classification in recent years. − Successful applications are presented in Table . The SSD series was competent to classify most kinds of domestic wastes at fairly high mAP with high detection speed, except for DSSD, which only detected targets at 2 fps .…”

Section: Development and Status Quo Of Sensor-based Waste Sorting Tec...mentioning

confidence: 99%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

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Section: Development and Status Quo Of Sensor-based Waste Sorting Tec...mentioning

confidence: 99%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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“…One of the most important datasets is the one gathered during the development of TrashNet [ 7 ]. This dataset has been the basis for many other studies [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. The TrashNet dataset has been further extended by adding the compost class [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

“…As the expected use of AWS systems is in apps in mobile phones [ 10 , 14 ] or in software embedded in waste picker robots [ 21 ], existing datasets [ 7 , 10 ] and, consequently, existing learned AWSs are no more adequate. Waste picker robots may be placed in urban as well as natural environments to clean and recycle precious waste using adapted AWS systems.…”

Section: Related Workmentioning

confidence: 99%

“…For example, waste pickers of the most important landfills in Brazil have been, or still are, subject to many diseases, due to working conditions: mainly osteo-muscular disorders, but also viral infections and emotional vulnerabilities [ 6 ]. Therefore, current waste recognition systems [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] as well as current corpora for training [ 7 , 10 ] focus on the activity of waste sorting in waste management plants. Solid waste runs on conveyor belts and, then, it is sorted into the different types.…”

Section: Introductionmentioning

confidence: 99%

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Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste

2021

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The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments.

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“…For the time being, several parties are working hard to improve waste recycling technologies since they believe it is one of the most effective ways to make a beneficial influence. There will be less trash to create, and the resource will last longer and be more sustainable as a result [2]. Furthermore, waste recycling can result in a variety of handicraft goods and a valuable business opportunity.…”

Section: Introductionmentioning

confidence: 99%

Waste Classification using CNN Algorithm

Diqi

2022

ICoSTEC

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One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.

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Valuable Waste Classification Modeling based on SSD-MobileNet

Cited by 13 publications

References 4 publications

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste

Waste Classification using CNN Algorithm

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