家庭日常工具的部件功用性主动认知是家庭服务机器人智能提升的重要方面。为满足服务机器人实时自主作业的需要，提出了一种基于结构随机森林(SRF)的工具部件功用性快速检测算法。在离线训练阶段，利用SRF训练功用性边缘检测器与功用性检测器，并通过评估功用性检测结果的Fβ值确定工具各部件功用性对应的先粗糙后逐步精细化(coarse-to-fine)阈值。在线检测阶段，首先使用功用性边缘检测器计算功用性区域边缘的初步概率图，继而加以coarse-to-fine阈值滤波得到包含工具部件功用性的外接矩形区域，最后对该区域使用功用性检测器进行检测。实验结果表明，在普通非图形处理器系统下，相较于现有的全局搜索检测方法，本文方法对各功用性部件的检测效率均明显提升，且召回率和精度都有提高。

Active cognition of affordance of household tool parts is regarded as an important aspect to improve home service robot intelligence. In order to meet the needs of real-time task of the service robot, a fast algorithm to improve the efficiency of affordance detection is proposed based on structured random forest (SRF). In the offline training phase, SRF is used to train affordance edge detector and affordance detector. Then the corresponding coarse-to-fine threshold of each affordance is determined by evaluating the results Fβof affordance detection. In the online detection phase, the affordance edge detector is used to calculate the initial probability map of the edge of affordance region. Then the coarse-to-fine threshold is used to obtain an outer rectangular including the region of the tool parts of corresponding affordance. Finally, the affordance detector is used to detect affordance of tool parts in the region obtained. The experimental results show that compared with the existing global search detection methods under normal non-grapnic processing unit systems, the average detection efficiency of the proposed method increases obviously, and the recall and precision are also improved.