基于深度优先随机森林分类器的目标检测

Object detection for depth-first random forest classifier

从机载视觉传感器获取的图像中检测近距离目标,对小型无人机飞行安全非常重要,需要大量样本训练分类器以提高目标检测的准确性。然而,如果训练样本太大,随着树的层数增加,广度优先方法训练随机森林分类器会导致欠拟合问题。针对这个问题,提出了深度优先方法递归训练随机森林分类器,每次递归过程只分裂一个节点。实验表明,在SenseAndAvoid数据集目标检测的平均准确率是69.3%,比广度优先方法训练的随机森林分类器高7.6%。深度优先方法递归训练随机森林分类器,能有效抑制广度优先方法训练时的欠拟合问题,提高了随机森林分类器的泛化能力和目标检测的准确性。

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.