CAJ | 학술논문

障碍物检测是智能机器人要解决的非结构复杂环境感知的典型问题之一.在实际情况中,获得大量未标记样本是相对容易的,而标记这些样本则是极其繁琐和费时的工作,当前的研究工作很少涉及到这类问题的解决办法.将SVM主动学习算法引入到障碍物检测中,针对常规的SVM主动学习算法在应用中所遇到的问题和局限性,采用一种动态聚类过程来选取最有代表性样本和根据专家标记与当前SVM分类结果的差值来调整SVM超平面位置的两种策略对其进行了改进,提出了一种新的主动学习算法--KSVMactiv算法,并在真实的野外环境图像库上进行了实验.由实验结果可知:KSVMactiv算法仅用81个样本就能达到很高的检测效果,从而说明它能显著减少数据标记的工作量,且与已有主动学习算法相比收敛速度更快.
장애물검측시지능궤기인요해결적비결구복잡배경감지적전형문제지일.재실제정황중,획득대량미표기양본시상대용역적,이표기저사양본칙시겁기번쇄화비시적공작,당전적연구공작흔소섭급도저류문제적해결판법.장SVM주동학습산법인입도장애물검측중,침대상규적SVM주동학습산법재응용중소우도적문제화국한성,채용일충동태취류과정래선취최유대표성양본화근거전가표기여당전SVM분류결과적차치래조정SVM초평면위치적량충책략대기진행료개진,제출료일충신적주동학습산법--KSVMactiv산법,병재진실적야외배경도상고상진행료실험.유실험결과가지:KSVMactiv산법부용81개양본취능체도흔고적검측효과,종이설명타능현저감소수거표기적공작량,차여이유주동학습산법상비수렴속도경쾌.
Obstacle detection is one of the tasks which are solved for intelligent robot in the unstructured complicated environment perception. Large amounts of training data are usually necessary in order to achieve satisfactory generalization, and attaining these training data is also relatively easy. While manually labeling data is an expensive and tedious process. The current research work related to the solutions of the above problems is also very limited. Active learning algorithm is introduced to obstacle detection here. Aiming at the problems and limitations in the process of applying general active learning algorithm, two strategies are used to improve general SVM active learning algorithm. These two strategies use a dynamic clustering to select the best representative samples and, according to the difference of expert's labeling and current SVM classification results, to tune the SVM hyperplane location. At the same time, a new SVM active learning algorithm is proposed, that is KSVMactive. Experiments are carried out in real wilderness environment image database. Experimental results demonstrate: very good detection results are obtained using KSVMactiv algorithm with only 81 samples, which can show that it can significantly reduce the workload of labeling data, and its convergence is better than other active learning algorithms.