基于改进Grassberger熵随机森林分类器的目标检测

Juanjuan Ma, Quan Pan, Yan Liang, Jinwen Hu, Chunhui Zhao, Yaning Guo

科研成果: 期刊稿件文章同行评审

13 引用 (Scopus)

摘要

Grassberger entropy is improved, and the improved Grassberger entropy is used to compute information gain. The random forest classifier is trained by selecting the optimal split parameters of the split node. The trained random forest classifier predicts whether the proposal windows generated by selective search contain object. For each of training samples and proposal windows, one normalized gradient magnitude, three LUV color channels, and six histograms of oriented gradients are extracted. The algorithm performance is tested on SenseAndAvoid dataset, and the average detection precision of 73.2% is achieved. Results show that the average detection precision is more than 98% in the range of safety envelope. The improved Grassberger entropy computing information gain can promote precision of object detection.

投稿的翻译标题Object Detection Based on Improved Grassberger Entropy Random Forest Classifier
源语言繁体中文
文章编号0704011
期刊Zhongguo Jiguang/Chinese Journal of Lasers
46
7
DOI
出版状态已出版 - 10 7月 2019

关键词

  • Improved Grassberger entropy
  • Information gain
  • Measurement
  • Object detection
  • Random forest classifier

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