 Dog recognition in public places based on convolutional neural networkJianquan Ouyang, Hao He, Yi He and Huanrong Tang International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 5, 1550147719829675 Abstract: 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%. Keywords: Convolutional neural network; dense–scale invariant feature transform; dog recognition (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:5:p:1550147719829675 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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