基于深度优先随机森林分类器的目标检测

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.