Wi-LADL: A Wireless-Based Lightweight Attention Deep Learning Method for Human–Vehicle Recognition

Wireless sensing-based human–vehicle recognition (HVR) methods have become a research hotspot due to their noninvasive, low-cost, and ubiquitous advantages. However, existing methods usually have limited applicability and low recognition performance. Furthermore, the antenna deployment height and the carrier frequency of wireless signals are also key factors that affect the HVR performance, but a few works deeply explore their impact on HVR tasks. To address these issues, inspired by the powerful feature learning capability of recently merged deep learning techniques, this article proposes an attention-based convolutional neural network (CNN) model named wireless-based lightweight attention deep learning (Wi-LADL) method for HVR. Wi-LADL takes received signal strength (RSS) as input since RSS contains the useful temporal feature of the gait for HVR. Wi-LADL consists of several lightweight CNN modules in series and a convolutional block attention module (CBAM) to learn high-level features from RSS signals. Experimental results on the developed five-category RSS dataset show that the proposed Wi-LADL not only achieves higher accuracy but also exhibits lower computational complexity compared with state-of-the-art HVR methods. In particular, Wi-LADL obtains the best performance with an average accuracy of 98.8% at the 2.4-GHz band and an antenna height of 0.8 m on HVR tasks. Specifically, when classifying one-pedestrian, two-pedestrians, one-bicycle, two-bicycles, and one-car, the proposed method achieves the recognition accuracy of 97.06%, 97.14%, 100%, 100%, and 100%, respectively. To facilitate the development of wireless sensing technology, the developed new RSS dataset has been publicly available at
https://github.com/TZ-mx/WiParam
.