Gradient clustering algorithm based on deep learning aerial image detection
| Autor: | Bin Guo, Ning Liu, Xinju Li, Xiangyu Min |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Deep learning Carry (arithmetic) Forest management 02 engineering and technology computer.software_genre 01 natural sciences Parallel processing (DSP implementation) Artificial Intelligence 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Data mining 010306 general physics Cluster analysis business computer Software Aerial image |
| Zdroj: | Pattern Recognition Letters. 141:37-44 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2020.09.032 |
| Popis: | In recent years, computer vision, especially deep learning, has been widely used in various fields. Through the deep learning aerial image detection gradient clustering algorithm automatic recognition, it can solve the limitations of manual shooting by humans, can shoot from a high altitude to a panoramic view of a specific area, and provide a more comprehensive solution. The traditional forest resource management and management work is mainly carried out by forestry personnel to carry out a large number of investigations and investigations on the forest. This method not only consumes a lot of manpower and material resources, but also does not have real-time nature. It is difficult to deal with all kinds of forest management. Problems, causing unnecessary losses. In this regard, this paper proposes an aerial image change detection algorithm based on H-KFCM, and designs related experiments to verify and demonstrate the performance of the algorithm. In this paper, we conduct a parallel study based on deep learning on the gradient clustering algorithm of deep learning in aerial image processing. By using CUDA (Compute Unified Device Architecture) to perform large-scale parallel processing of aerial data. Can greatly shorten the time to obtain results, improve the efficiency of relevant personnel. Experiment analysis. It can be seen from the results that the deep learning parallelization program implemented in this paper has a faster calculation speed and uses less time in high-resolution images, and has a good acceleration ratio compared to the CPU. |
| Databáze: | OpenAIRE |
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