Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

Symmetry

João Pedro Matos-Carvalho¹  Sérgio Duarte Correia¹  Tiago Simas¹  Iuliia Kim²  Ilya Viksnin² 

[1] COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal;Mobile Intelligent Systems Laboratory, Saint Petersburg Electrotechnical University “LETI”, 5 Professor Popov Street, 197376 Saint Petersburg, Russia;

关键词: computer vision;    particle swarm optimization;    unmanned aerial vehicle;    Gray-Level Co-Occurrence Matrix;    classification;    image analysis;   

DOI  :  10.3390/sym14061080

来源: DOAJ

【 摘 要 】

Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a methodology for terrain classification. The overall classification procedure consists of the following steps: (1) pre-processing, (2) feature extraction, and (3) classification. For the pre-processing stage, a clustering method based on particle swarm optimization was elaborated, which helps to extract object patterns from the image. Feature extraction is conducted via Gray-Level Co-Occurrence Matrix calculation, and the output of the matrix is turned into the input for a feed-forward neural network classification stage. The developed computer vision system showed 88.7% accuracy on the selected test set. These results can provide high quality territory monitoring; prospectively, we plan to establish a self-positioning system based on computer vision.

【 授权许可】

Unknown