| Autor: |
Kunrong Zhao, Tingting He, Shuang Wu, Songling Wang, Bilan Dai, Qifan Yang, Yutao Lei |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Image and Video Processing, Vol 2018, Iss 1, Pp 1-11 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1687-5281 |
| DOI: |
10.1186/s13640-018-0391-6 |
| Popis: |
Abstract UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer vision. Convolution neural network model is the most commonly used image processing model. Compared with the traditional artificial neural network model, convolution neural network has more hidden layers. Its unique convolution and pooling operations have higher efficiency in image processing. It has incomparable advantages in image recognition and location and other forms of two-dimensional graphics tasks. As a new deformation of convolution neural network, residual neural network aims to make convolution layer learn a kind of residual instead of a direct learning goal. After analyzing the characteristics of CNN model for image feature representation and residual network, a residual network model is built. The UAV remote sensing system is selected as the platform to acquire image data, and the problem of image recognition based on residual neural network is studied, which is verified by experiment simulation and precision analysis. Finally, the problems and experiences in the process of learning and designing are discussed, and the future improvements in the field of image target location and recognition are prospected. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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