Application of target detection method based on convolutional neural network in sustainable outdoor education
In order to realize the intelligence of underwater robots, this exploration proposes a submersible vision system based on neurorobotics to obtain the target information in underwater camera data. This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine...
| Main Authors: | , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106645/ |
| _version_ | 1848864797212278784 |
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| author | Yang, Xiaoming Samsudin, Shamsulariffin Wang, Yuxuan Yuan, Yubin Tengku Kamalden, Tengku Fadilah Yaakob, Sam Shor Nahar |
| author_facet | Yang, Xiaoming Samsudin, Shamsulariffin Wang, Yuxuan Yuan, Yubin Tengku Kamalden, Tengku Fadilah Yaakob, Sam Shor Nahar |
| author_sort | Yang, Xiaoming |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In order to realize the intelligence of underwater robots, this exploration proposes a submersible vision system based on neurorobotics to obtain the target information in underwater camera data. This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine the target information in underwater camera data. First, the underwater functions of the manned submersible are analyzed and mined to obtain the specific objects and features of the underwater camera information. Next, the dataset of the specific underwater target image is further constructed. The acquisition system of underwater camera information of manned submersibles is designed through the Single Shot-MultiBox Detector algorithm of deep learning. Furthermore, CNN is adopted to classify the underwater target images, which realizes the intelligent detection and classification of underwater targets. Finally, the models performance is tested through experiments, and the following conclusions are obtained. The model can recognize underwater organisms local, global, and visual features. Different recognition methods have certain advantages in accuracy, speed, and other aspects. The design here integrates deep learning technology and computer vision technology and applies it to the underwater field, realizing the association of the identified biological information with the geographic information and marine information. This is of great significance to realize the multi-information fusion of manned submersibles and the intelligent field of outdoor education. The contribution of this exploration is to provide a reasonable direction for the intelligent development of outdoor diving education. |
| first_indexed | 2025-11-15T13:54:31Z |
| format | Article |
| id | upm-106645 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:54:31Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1066452024-08-16T08:10:24Z http://psasir.upm.edu.my/id/eprint/106645/ Application of target detection method based on convolutional neural network in sustainable outdoor education Yang, Xiaoming Samsudin, Shamsulariffin Wang, Yuxuan Yuan, Yubin Tengku Kamalden, Tengku Fadilah Yaakob, Sam Shor Nahar In order to realize the intelligence of underwater robots, this exploration proposes a submersible vision system based on neurorobotics to obtain the target information in underwater camera data. This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine the target information in underwater camera data. First, the underwater functions of the manned submersible are analyzed and mined to obtain the specific objects and features of the underwater camera information. Next, the dataset of the specific underwater target image is further constructed. The acquisition system of underwater camera information of manned submersibles is designed through the Single Shot-MultiBox Detector algorithm of deep learning. Furthermore, CNN is adopted to classify the underwater target images, which realizes the intelligent detection and classification of underwater targets. Finally, the models performance is tested through experiments, and the following conclusions are obtained. The model can recognize underwater organisms local, global, and visual features. Different recognition methods have certain advantages in accuracy, speed, and other aspects. The design here integrates deep learning technology and computer vision technology and applies it to the underwater field, realizing the association of the identified biological information with the geographic information and marine information. This is of great significance to realize the multi-information fusion of manned submersibles and the intelligent field of outdoor education. The contribution of this exploration is to provide a reasonable direction for the intelligent development of outdoor diving education. Multidisciplinary Digital Publishing Institute 2023-01-31 Article PeerReviewed Yang, Xiaoming and Samsudin, Shamsulariffin and Wang, Yuxuan and Yuan, Yubin and Tengku Kamalden, Tengku Fadilah and Yaakob, Sam Shor Nahar (2023) Application of target detection method based on convolutional neural network in sustainable outdoor education. Sustainability, 15 (3). art. no. 2542. pp. 1-21. ISSN 2071-1050 https://www.mdpi.com/2071-1050/15/3/2542 10.3390/su15032542 |
| spellingShingle | Yang, Xiaoming Samsudin, Shamsulariffin Wang, Yuxuan Yuan, Yubin Tengku Kamalden, Tengku Fadilah Yaakob, Sam Shor Nahar Application of target detection method based on convolutional neural network in sustainable outdoor education |
| title | Application of target detection method based on convolutional neural network in sustainable outdoor education |
| title_full | Application of target detection method based on convolutional neural network in sustainable outdoor education |
| title_fullStr | Application of target detection method based on convolutional neural network in sustainable outdoor education |
| title_full_unstemmed | Application of target detection method based on convolutional neural network in sustainable outdoor education |
| title_short | Application of target detection method based on convolutional neural network in sustainable outdoor education |
| title_sort | application of target detection method based on convolutional neural network in sustainable outdoor education |
| url | http://psasir.upm.edu.my/id/eprint/106645/ http://psasir.upm.edu.my/id/eprint/106645/ http://psasir.upm.edu.my/id/eprint/106645/ |