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
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/9349/ http://eprints.uthm.edu.my/9349/1/J15842_a4b08ae6371acab3a7a9751138b4a414.pdf |
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| author | Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Jain, Antima Deevesh Chaudhary, Deevesh Chaudhary Shamik Tiwari, Shamik Tiwari Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan |
| author_facet | Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Jain, Antima Deevesh Chaudhary, Deevesh Chaudhary Shamik Tiwari, Shamik Tiwari Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan |
| author_sort | Akhilesh Kumar Sharma, Akhilesh Kumar Sharma |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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 | 2025-11-15T20:29:35Z |
| format | Article |
| id | uthm-9349 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:29:35Z |
| publishDate | 2023 |
| publisher | IJACSA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-93492023-07-17T07:51:18Z http://eprints.uthm.edu.my/9349/ An Approach to Automatic Garbage Detection Framework Designing using CNN Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Jain, Antima Deevesh Chaudhary, Deevesh Chaudhary Shamik Tiwari, Shamik Tiwari Mahdin, Hairulnizam Baharum, Zirawani Shaharudin, Shazlyn Milleana Maskat, Ruhaila Arshad, Mohammad Syafwan T Technology (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 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9349/1/J15842_a4b08ae6371acab3a7a9751138b4a414.pdf Akhilesh Kumar Sharma, Akhilesh Kumar Sharma and Jain, Antima and Deevesh Chaudhary, Deevesh Chaudhary and Shamik Tiwari, Shamik Tiwari and Mahdin, Hairulnizam and Baharum, Zirawani and Shaharudin, Shazlyn Milleana and Maskat, Ruhaila and Arshad, Mohammad Syafwan (2023) An Approach to Automatic Garbage Detection Framework Designing using CNN. International Journal of Advanced Computer Science and Applications, 14 (2). pp. 257-262. |
| spellingShingle | T Technology (General) Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Jain, Antima Deevesh Chaudhary, Deevesh Chaudhary Shamik Tiwari, Shamik Tiwari 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 | T Technology (General) |
| url | http://eprints.uthm.edu.my/9349/ http://eprints.uthm.edu.my/9349/1/J15842_a4b08ae6371acab3a7a9751138b4a414.pdf |