基于深度优先随机森林分类器的目标检测

Juanjuan Ma, Quan Pan, Yan Liang, Jinwen Hu, Chunhui Zhao, Huaxia Wang

科研成果: 期刊稿件文章同行评审

7 引用 (Scopus)

摘要

The ability to detect the visible objects from the images obtained by onboard vision sensors is very important for flight security of small unmanned aerial vehicle. A great number of samples are needed to train the classifier to improve the precision of object detection. However, breadth-first random forest classifier training will lead to underfitting when the number of tree layers increases. To solve this problem, depth-first is injected into random forest classifier for implementing the tree training, where only one node is split at each recursive time. Experiments demonstrate that the detection average precision on SenseAndAvoid dataset is 69.3%, which improves the average precision by more than 7.6% compared with that of breadth-first random forest classifier training. Depth-first random forest classifier training is able to effectively inhibit underfitting, which improves the generalization performance of random forest classifier and the precision of object detection.

投稿的翻译标题Object detection for depth-first random forest classifier
源语言繁体中文
页(从-至)518-523
页数6
期刊Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology
26
4
DOI
出版状态已出版 - 1 8月 2018

关键词

  • Depth first
  • Object detection
  • Random forest classifier
  • Unmanned aerial vehicle

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