中国激光, 2019, 46 (7): 0704011, 网络出版: 2019-07-11
基于改进Grassberger熵随机森林分类器的目标检测 下载: 993次
Object Detection Based on Improved Grassberger Entropy Random Forest Classifier
测量 目标检测 改进的Grassberger熵 随机森林分类器 信息增益 measurement object detection improved Grassberger entropy random forest classifier information gain
摘要
对Grassberger熵进行改进,采用改进的Grassberger熵计算信息增益,选择分裂节点的最优分裂属性训练随机森林分类器,利用经过训练的随机森林分类器预测选择性搜索生成的子窗口是否包含目标。对每个训练样本及子窗口提取1个归一化梯度幅值、3个LUV颜色通道和6个梯度方向直方图的特征。在SenseAndAvoid数据集上测试了所提方法的性能,取得了73.2%的平均检测准确率。结果表明:安全包络范围内的平均检测准确率高于98%。利用改进的Grassberger熵计算信息增益,能提高目标检测的准确率。
Abstract
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.
马娟娟, 潘泉, 梁彦, 胡劲文, 赵春晖, 郭亚宁. 基于改进Grassberger熵随机森林分类器的目标检测[J]. 中国激光, 2019, 46(7): 0704011. Juanjuan Ma, Quan Pan, Yan Liang, Jinwen Hu, Chunhui Zhao, Yaning Guo. Object Detection Based on Improved Grassberger Entropy Random Forest Classifier[J]. Chinese Journal of Lasers, 2019, 46(7): 0704011.