基于 RSVP 的面向不良信息检测人机协作系统研究

针对复杂的环境背景下不良信息的快速准确检测问题,提出了基于快速序列视觉呈现( rapid serial visual presentation, RSVP)的面向不良信息检测人机协作系统。 首先利用快速佩戴便携式采集系统采集了 12 名受试者的脑电数据;然后采用 Mallat 算法提取较低维度的时频特征,使用人工神经网络(ANN)和支持向量机(SVM)两种模型分类对比;最后在训练集中引入 不同次数的叠加平均数据以改善模型的分类性能。 实验结果表明,在含有 3 个目标的 60 张图像中平均正确输出至少 2 张目 标,AUC 值达到了 0. 9。 该系统在小批量数据集、环境变化复杂的不良图像信息检测中有着良好的性能,相较于人工检测提高 了效率。

Research on human-machine cooperation system for bad information detection based on RSVP

Aiming at the problem of fast and accurate detection of bad information under complex environment background, a humanmachine collaboration system for bad information detection based on rapid serial visual presentation (RSVP) is proposed. Firstly, using the fast-wearing portable acquisition system collected the EEG data of 12 subjects; then the Mallat algorithm was used to extract the lower-dimensional time-frequency features of the EEG data, and EEG signal classification uses artificial neural network (ANN) and support vector machine (SVM). Finally, different times of superimposed average data are introduced in the training set to improve the classification performance of the model. The experimental results show that at least 2 targets are correctly output on average in 60 images containing 3 targets, and the AUC value reaches 0. 9. The system has good performance in the detection of small batch data sets and bad image information with complex environmental changes, and has improved efficiency compared with manual detection.