An Approach to Automatic Garbage Detection Framework Designing using CNN
This paper proposes a system for automatic detection of litter and garbage dumps in CCTV feeds with the help of deep learning implementations. The designed system named Greenlock scans and identifies entities that resemble an accumulation of garbage or a garbage dump in real time and alerts the re...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Ijacsa
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| Online Access: | http://eprints.uthm.edu.my/8813/1/J15842_a4b08ae6371acab3a7a9751138b4a414.pdf |
| _version_ | 1825710644596310016 |
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| author | Kumar Sharma, Akhilesh Jain, Antima Chaudhary, Deevesh Tiwari, Shamik Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan |
| author_facet | Kumar Sharma, Akhilesh Jain, Antima Chaudhary, Deevesh Tiwari, Shamik Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan |
| author_sort | Kumar Sharma, Akhilesh |
| collection | UTHM |
| description | This paper proposes a system for automatic detection of litter and garbage dumps in CCTV feeds with the help of deep learning implementations. The designed system named Greenlock scans and identifies entities that resemble an
accumulation of garbage or a garbage dump in real time and
alerts the respective authorities to deal with the issue by locating the point of origin. The entity is labelled as garbage if it passes a certain similarity threshold. ResNet-50 has been used for the training purpose alongside TensorFlow for mathematical operations for the neural network. Combined with a pre-existing CCTV surveillance system, this system has the capability to hugely minimize garbage management costs via the prevention of formation of big dumps. The automatic detection also saves the manpower required in manual surveillance and contributes towards healthy neighborhoods and cleaner cities. This article is also showing the comparison between applied various algorithms such as standard TensorFlow, inception algo and faster-r CNN and Resnet-50, and it has been observed that Resnet-50 performed with better accuracy. The study performed here proved to be a stress reliever in terms of the garbage identification and dumping for any country. At the end of the article the comparison chart has been shown. |
| first_indexed | 2024-03-05T22:00:45Z |
| format | Article |
| id | uthm.eprints-8813 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:00:45Z |
| publisher | Ijacsa |
| record_format | dspace |
| spelling | uthm.eprints-88132023-06-14T00:43:41Z http://eprints.uthm.edu.my/8813/ An Approach to Automatic Garbage Detection Framework Designing using CNN Kumar Sharma, Akhilesh Jain, Antima Chaudhary, Deevesh Tiwari, Shamik Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) This paper proposes a system for automatic detection of litter and garbage dumps in CCTV feeds with the help of deep learning implementations. The designed system named Greenlock scans and identifies entities that resemble an accumulation of garbage or a garbage dump in real time and alerts the respective authorities to deal with the issue by locating the point of origin. The entity is labelled as garbage if it passes a certain similarity threshold. ResNet-50 has been used for the training purpose alongside TensorFlow for mathematical operations for the neural network. Combined with a pre-existing CCTV surveillance system, this system has the capability to hugely minimize garbage management costs via the prevention of formation of big dumps. The automatic detection also saves the manpower required in manual surveillance and contributes towards healthy neighborhoods and cleaner cities. This article is also showing the comparison between applied various algorithms such as standard TensorFlow, inception algo and faster-r CNN and Resnet-50, and it has been observed that Resnet-50 performed with better accuracy. The study performed here proved to be a stress reliever in terms of the garbage identification and dumping for any country. At the end of the article the comparison chart has been shown. Ijacsa Article PeerReviewed text en http://eprints.uthm.edu.my/8813/1/J15842_a4b08ae6371acab3a7a9751138b4a414.pdf Kumar Sharma, Akhilesh and Jain, Antima and Chaudhary, Deevesh and Tiwari, Shamik and Mahdin, Hairulnizam and Baharum, Zirawani and Shaharudin, Shazlyn Milleana and Maskat, Ruhaila and Arshad, Mohammad Syafwan An Approach to Automatic Garbage Detection Framework Designing using CNN. -, 14 (2). pp. 257-262. |
| spellingShingle | TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Kumar Sharma, Akhilesh Jain, Antima Chaudhary, Deevesh Tiwari, Shamik Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan An Approach to Automatic Garbage Detection Framework Designing using CNN |
| title | An Approach to Automatic Garbage Detection Framework Designing using CNN |
| title_full | An Approach to Automatic Garbage Detection Framework Designing using CNN |
| title_fullStr | An Approach to Automatic Garbage Detection Framework Designing using CNN |
| title_full_unstemmed | An Approach to Automatic Garbage Detection Framework Designing using CNN |
| title_short | An Approach to Automatic Garbage Detection Framework Designing using CNN |
| title_sort | approach to automatic garbage detection framework designing using cnn |
| topic | TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) |
| url | http://eprints.uthm.edu.my/8813/1/J15842_a4b08ae6371acab3a7a9751138b4a414.pdf |
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