自动化学报2018，Vol.44Issue(12)：2238-2249,12.DOI:10.16383/j.aas.2018.c170265
改进的YOLO特征提取算法及其在服务机器人隐私情境检测中的应用
An Improved YOLO Feature Extraction Algorithm and Its Application to Privacy Situation Detection of Social Robots
摘要
Abstract
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.关键词
YOLO/特征提取算法/服务机器人/隐私情境检测/智能家居Key words
YOLO/feature extraction algorithm/social robot/detection of privacy situations/smart homes引用本文复制引用
杨观赐,杨静,苏志东,陈占杰..改进的YOLO特征提取算法及其在服务机器人隐私情境检测中的应用[J].自动化学报,2018,44(12):2238-2249,12.基金项目
国家自然科学基金(61863005,61640209) （61863005,61640209）
贵州省科技计划项目(黔科合人字(2015)13,黔科合JZ字[2014]2004,黔科合LH字[2016]7433,黔科合平台人才[2018] 5702) （黔科合人字(2015）
贵州省教育厅研究生教改重点课题(黔教研合JG字[2015] 002)资助 （黔教研合JG字[2015] 002）
