Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset
Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep lea...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2020-12-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/66597 |
| _version_ | 1811247959634870272 |
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| author | Yonatan Adiwinata Akane Sasaoka I Putu Agung Bayupati Oka Sudana |
| author_facet | Yonatan Adiwinata Akane Sasaoka I Putu Agung Bayupati Oka Sudana |
| author_sort | Yonatan Adiwinata |
| collection | DOAJ |
| description | Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%. |
| first_indexed | 2024-04-12T15:18:31Z |
| format | Article |
| id | doaj.art-7e32f816ec924b37bb9e397f8b7b9f7d |
| institution | Directory Open Access Journal |
| issn | 2088-1541 2541-5832 |
| language | English |
| last_indexed | 2024-04-12T15:18:31Z |
| publishDate | 2020-12-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj.art-7e32f816ec924b37bb9e397f8b7b9f7d2022-12-22T03:27:32ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322020-12-0111314415410.24843/LKJITI.2020.v11.i03.p0366597Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH DatasetYonatan Adiwinata0Akane Sasaoka1I Putu Agung Bayupati2Oka Sudana3Department of Information Technology, Faculty of Engineering, Udayana UniversityElectrical Engineering and Computer Sciense, Kanazawa UniversityDepartment of Information Technology, Faculty of Engineering, Udayana UniversityDepartment of Information Technology, Faculty of Engineering, Udayana UniversityFish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%.https://ojs.unud.ac.id/index.php/lontar/article/view/66597 |
| spellingShingle | Yonatan Adiwinata Akane Sasaoka I Putu Agung Bayupati Oka Sudana Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset Lontar Komputer |
| title | Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset |
| title_full | Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset |
| title_fullStr | Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset |
| title_full_unstemmed | Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset |
| title_short | Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset |
| title_sort | fish species recognition with faster r cnn inception v2 using qut fish dataset |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/66597 |
| work_keys_str_mv | AT yonatanadiwinata fishspeciesrecognitionwithfasterrcnninceptionv2usingqutfishdataset AT akanesasaoka fishspeciesrecognitionwithfasterrcnninceptionv2usingqutfishdataset AT iputuagungbayupati fishspeciesrecognitionwithfasterrcnninceptionv2usingqutfishdataset AT okasudana fishspeciesrecognitionwithfasterrcnninceptionv2usingqutfishdataset |