Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
USB Palm Oil Counting (Unstripped Bunch) is important in oil palm processing mills. Information on the number of USBs is essential because it shows the level of efficiency of the palm oil processing plant. Due to its complexity, palm oil mills rely solely on manual calculations, which is inefficient...
| Hlavní autoři: | , , |
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| Médium: | Conference or Workshop Item |
| Jazyk: | English English |
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IEEE
2021
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| On-line přístup: | http://umpir.ump.edu.my/id/eprint/41993/1/Automatic%20Oil%20Palm%20Unstripped%20Bunch_ABST.pdf http://umpir.ump.edu.my/id/eprint/41993/2/Automatic%20Oil%20Palm%20Unstripped%20Bunch.pdf |
| _version_ | 1825815796078608384 |
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| author | Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son Ali, Akbar |
| author_facet | Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son Ali, Akbar |
| author_sort | Wahyu, Sapto Aji |
| collection | UMP |
| description | USB Palm Oil Counting (Unstripped Bunch) is important in oil palm processing mills. Information on the number of USBs is essential because it shows the level of efficiency of the palm oil processing plant. Due to its complexity, palm oil mills rely solely on manual calculations, which is inefficient workforce waste. Challenging aspects such as partial occlusion, overlap, and even different perspectives limit the use of traditional computer vision techniques. In recent years, deep learning has become increasingly popular for computer vision applications due to its superior performance over conventional methods. This paper proposes a deep learning solution to solve USB computing problems in palm oil mills. Our proposed automated USB counter system consists of an object detector built on the RCNN Faster architecture and an object tracker made in the euclidean distance. Our proposed system identifies and counts USBs with an average accuracy of 71.5% in testing. |
| first_indexed | 2024-09-25T03:51:39Z |
| format | Conference or Workshop Item |
| id | UMPir41993 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:51:39Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir419932024-07-18T02:53:41Z http://umpir.ump.edu.my/id/eprint/41993/ Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son Ali, Akbar TK Electrical engineering. Electronics Nuclear engineering USB Palm Oil Counting (Unstripped Bunch) is important in oil palm processing mills. Information on the number of USBs is essential because it shows the level of efficiency of the palm oil processing plant. Due to its complexity, palm oil mills rely solely on manual calculations, which is inefficient workforce waste. Challenging aspects such as partial occlusion, overlap, and even different perspectives limit the use of traditional computer vision techniques. In recent years, deep learning has become increasingly popular for computer vision applications due to its superior performance over conventional methods. This paper proposes a deep learning solution to solve USB computing problems in palm oil mills. Our proposed automated USB counter system consists of an object detector built on the RCNN Faster architecture and an object tracker made in the euclidean distance. Our proposed system identifies and counts USBs with an average accuracy of 71.5% in testing. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41993/1/Automatic%20Oil%20Palm%20Unstripped%20Bunch_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/41993/2/Automatic%20Oil%20Palm%20Unstripped%20Bunch.pdf Wahyu, Sapto Aji and Kamarul Hawari, Ghazali and Son Ali, Akbar (2021) Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking. In: 2nd International Conference on Computer Science and Engineering: The Effects of the Digital World After Pandemic (EDWAP), IC2SE 2021. 2nd International Conference on Computer Science and Engineering, IC2SE 2021 , 16 - 18 November 2021 , Padang, Indonesia. pp. 1-5.. ISBN 978-166540045-9 (Published) https://doi.org/10.1109/IC2SE52832.2021.9792068 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son Ali, Akbar Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking |
| title | Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking |
| title_full | Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking |
| title_fullStr | Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking |
| title_full_unstemmed | Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking |
| title_short | Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking |
| title_sort | automatic oil palm unstripped bunch usb counting system based on faster rcnn and object tracking |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/41993/1/Automatic%20Oil%20Palm%20Unstripped%20Bunch_ABST.pdf http://umpir.ump.edu.my/id/eprint/41993/2/Automatic%20Oil%20Palm%20Unstripped%20Bunch.pdf |
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