UAV Target Detection for IoT via Enhancing ERP Component by Brain–Computer Interface System
Yufeng Zhang, Hongxin Zhang, Xiaorong Gao, Shangen Zhang, Chen Yang
Abstract
The increasing popularity of Internet of Things (IoT) devices provides a huge data source for intelligent identification. Images captured by the unmanned aerial vehicle (UAV) are often exploited in target detection missions for search, rescue and fire prevention. However, without sufficient training samples, the machine learning method is usually difficult to meet application requirements. To solve the problem, a brain–computer interface (BCI) real-time system is applied to UAV target detection. In this study, a novel rapid serial visual presentation (RSVP) paradigm was formulated to present images, enhancing the ability for wide-area target detection. Since there is spatial correlation in captured images, joint decision for identical target can improve the recognition efficiency. To suppress interfering components and improve event-related potential (ERP) detection efficiency, an enhancing ERP component (EEC) algorithm is proposed. Both decision method and EEC algorithm are based on strictly statistical theory. RSVP task was performed by 12 subjects. The interference components and noise correlation were significantly reduced by the EEC algorithm. The target detection rate online was 86.6% while the false alarm rate was less than 5%. Besides, the joint decision strategy raised the area under curve (AUC) value from 0.876 to 0.963. The proposed BCI real-time system realizes the complementarity of human intelligence and IoT, ushering UAV target detection into the era of hybrid intelligence.