To address the limitation of YOLO algorithm in recognizing small objects and information loss during feature extraction, we propose FYOLO, an improved feature extraction algorithm based on YOLO. The algorithm uses a novel neural network structure inspired by the deformable parts model (DPM) and region-based fully convolutional networks (R-FCN). A sliding window merging algorithm based on region proposal networks (RPN) is then combined with the neural network to form the FYOLO algorithm. To evaluate the performance of the proposed algorithm, we develop a social robot platform for privacy situation detection. We consider six types of situations in a smart home and prepare three datasets including training dataset, validation dataset, and test dataset. Experimental parameters such as training step and learning rate are set in terms of their relationships with the prediction accuracy. Extensive privacy situation detection experiments on the social robot show that FYOLO is capable of recognizing privacy situations with an accuracy of 94.48 ％, indicating the good robustness of our FYOLO algorithm. Finally, the comparison results between FYOLO and YOLO show that the proposed FYOLO outperforms YOLO in recognition accuracy.%为了提高YOLO识别较小目标的能力, 解决其在特征提取过程中的信息丢失问题, 提出改进的YOLO特征提取算法.将目标检测方法 DPM与R-FCN融入到YOLO中, 设计一种改进的神经网络结构, 包含一个全连接层以及先池化再卷积的特征提取模式以减少特征信息的丢失.然后, 设计基于RPN的滑动窗口合并算法, 进而形成基于改进YOLO的特征提取算法.搭建服务机器人情境检测平台, 给出服务机器人情境检测的总体工作流程.设计家居环境下的六类情境, 建立训练数据集、验证数据集和4类测试数据集.测试分析训练步骤与预测概率估计值、学习率与识别准确性之间的关系, 找出了适合所提出算法的训练步骤与学习率的经验值.测试结果表明:所提出的算法隐私情境检测准确率为94.48％, 有较强的识别鲁棒性.最后, 与YOLO算法的比较结果表明, 本文算法在识别准确率方面优于YOLO算法.
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