Dog recognition in public places based on convolutional neural network
With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a n...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719829675 |
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| _version_ | 1832547787397398528 |
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| author | Jianquan Ouyang Hao He Yi He Huanrong Tang |
| author_facet | Jianquan Ouyang Hao He Yi He Huanrong Tang |
| author_sort | Jianquan Ouyang |
| collection | DOAJ |
| description | With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%. |
| format | Article |
| id | doaj-art-7e346ea528904ac89ea7f37d56bd5a44 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7e346ea528904ac89ea7f37d56bd5a442025-02-03T06:43:16ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-05-011510.1177/1550147719829675Dog recognition in public places based on convolutional neural networkJianquan Ouyang0Hao He1Yi He2Huanrong Tang3Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, College of Information Engineering, Xiangtan University, Xiangtan, ChinaKey Laboratory of Intelligent Computing and Information Processing, Ministry of Education, College of Information Engineering, Xiangtan University, Xiangtan, ChinaKey Laboratory of Intelligent Computing and Information Processing, Ministry of Education, College of Information Engineering, Xiangtan University, Xiangtan, ChinaKey Laboratory of Intelligent Computing and Information Processing, Ministry of Education, College of Information Engineering, Xiangtan University, Xiangtan, ChinaWith the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%.https://doi.org/10.1177/1550147719829675 |
| spellingShingle | Jianquan Ouyang Hao He Yi He Huanrong Tang Dog recognition in public places based on convolutional neural network International Journal of Distributed Sensor Networks |
| title | Dog recognition in public places based on convolutional neural network |
| title_full | Dog recognition in public places based on convolutional neural network |
| title_fullStr | Dog recognition in public places based on convolutional neural network |
| title_full_unstemmed | Dog recognition in public places based on convolutional neural network |
| title_short | Dog recognition in public places based on convolutional neural network |
| title_sort | dog recognition in public places based on convolutional neural network |
| url | https://doi.org/10.1177/1550147719829675 |
| work_keys_str_mv | AT jianquanouyang dogrecognitioninpublicplacesbasedonconvolutionalneuralnetwork AT haohe dogrecognitioninpublicplacesbasedonconvolutionalneuralnetwork AT yihe dogrecognitioninpublicplacesbasedonconvolutionalneuralnetwork AT huanrongtang dogrecognitioninpublicplacesbasedonconvolutionalneuralnetwork |