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- W2364587745 abstract "The autonomous obstacle detection and avoidance of unmanned aerial vehicle(UAV) in low-level flight is a key technology to ensure its survivability.Therefore,it is important to develop the technology.After the study of the computer vision principle and off-line supervised learning,since the consideration that the support vector machine(SVM) has the properties in generalization and real-time performance,and can reduce the matching difficulty and computation quantity at the same time,SVM is used to segment a forward-looking image into two parts(sky and non-sky),in which the non-sky part is considered as the obstacle that UAV should avoid.A vision-based obstacle detection system was achieved for UAVs and the information for subsequent visual navigation was provide.The experimental results indicate that SVM can segment forward-looking images accurately and has good generalization performance." @default.
- W2364587745 created "2016-06-24" @default.
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- W2364587745 date "2011-01-01" @default.
- W2364587745 modified "2023-09-24" @default.
- W2364587745 title "Vision Obstacle Detection for UAV Based on Support Vector Machine" @default.
- W2364587745 hasPublicationYear "2011" @default.
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