A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management
The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management syste...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/1/14 |
| _version_ | 1797543646534303744 |
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| author | Saurav Kumar Drishti Yadav Himanshu Gupta Om Prakash Verma Irshad Ahmad Ansari Chang Wook Ahn |
| author_facet | Saurav Kumar Drishti Yadav Himanshu Gupta Om Prakash Verma Irshad Ahmad Ansari Chang Wook Ahn |
| author_sort | Saurav Kumar |
| collection | DOAJ |
| description | The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items. |
| first_indexed | 2024-03-10T13:48:35Z |
| format | Article |
| id | doaj.art-9fd6b7789c9d4ea7ae537af5b65ea291 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T13:48:35Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9fd6b7789c9d4ea7ae537af5b65ea2912023-11-21T02:23:16ZengMDPI AGElectronics2079-92922020-12-011011410.3390/electronics10010014A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste ManagementSaurav Kumar0Drishti Yadav1Himanshu Gupta2Om Prakash Verma3Irshad Ahmad Ansari4Chang Wook Ahn5Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Punjab 144011, IndiaDepartment of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Punjab 144011, IndiaDepartment of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Punjab 144011, IndiaDepartment of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Punjab 144011, IndiaElectronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, IndiaAI Graduate School, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaThe colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.https://www.mdpi.com/2079-9292/10/1/14Convolutional Neural Network (CNN)deep learningobject detectionwaste managementwaste segregationYOLOv3 algorithm |
| spellingShingle | Saurav Kumar Drishti Yadav Himanshu Gupta Om Prakash Verma Irshad Ahmad Ansari Chang Wook Ahn A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management Electronics Convolutional Neural Network (CNN) deep learning object detection waste management waste segregation YOLOv3 algorithm |
| title | A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management |
| title_full | A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management |
| title_fullStr | A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management |
| title_full_unstemmed | A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management |
| title_short | A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management |
| title_sort | novel yolov3 algorithm based deep learning approach for waste segregation towards smart waste management |
| topic | Convolutional Neural Network (CNN) deep learning object detection waste management waste segregation YOLOv3 algorithm |
| url | https://www.mdpi.com/2079-9292/10/1/14 |
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