Application of Target Detection Method Based on Convolutional Neural Network in Sustainable Outdoor Education

Xiaoming Yang, Shamsulariffin Samsudin (), Yuxuan Wang, Yubin Yuan, Tengku Fadilah Tengku Kamalden and Sam Shor Nahar bin Yaakob
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Xiaoming Yang: Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Shamsulariffin Samsudin: Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Yuxuan Wang: Sports Institute, Nangchang Jiao Tong Institute, Nanchang 330100, China
Yubin Yuan: Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Tengku Fadilah Tengku Kamalden: Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Sam Shor Nahar bin Yaakob: Department of Nature Parks and Recreation, Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Malaysia

Sustainability, 2023, vol. 15, issue 3, 1-21

Abstract: 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 model’s 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.

Keywords: neural network algorithm; neurorobotics; manned submersible; outdoor education; convolutional neural network; image processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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