Different speech detection sensors have been developed over the years but they are limited by the loss of high frequency speech energy, and have restricted non-contact detection due to the lack of penetrability. This paper proposes a novel millimeter microwave radar sensor to detect speech signals. The utilization of a high operating frequency and a superheterodyne receiver contributes to the high sensitivity of the radar sensor for small sound vibrations. In addition, the penetrability of microwaves allows the novel sensor to detect speech signals through nonmetal barriers. Results show that the novel sensor can detect high frequency speech energies and that the speech quality is comparable to traditional microphone speech. Moreover, the novel sensor can detect speech signals through a nonmetal material of a certain thickness between the sensor and the subject. Thus, the novel speech sensor expands traditional speech detection techniques and provides an exciting alternative for broader application prospects.
Pedestrian detection is a classic problem in computer vision, which has an essential impact on the safety of urban autonomous driving. Although significant improvement has been made in pedestrian detection recently, small-scale pedestrian detection is still challenging. To effectively tackle this issue, a multi-scale pedestrian detector based on self-attention mechanism and adaptive spatial feature fusion is proposed in this paper. In order to better extract global information, the spatial attention mechanism asymmetric pyramid non-local block (APNB) module is applied. To achieve scale-invariance detection, multiple detection branches are designed, which include a high-resolution detection branch and a lowresolution detection branch. In integrating multi-scale features, the adaptively spatial feature fusion (ASFF) method is employed, which can solve the problem of feature inconsistency across different scales. Experimental results show that the proposed method obtains competitive performance on Caltech and CityPersons datasets.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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