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Deep learning technology enables faster and more accurate terahertz security inspection


Improved SSD community structure. Credit: Scientific Studies (2022). DOI: 10.1038/s41598-022-16208-0

With the strengthening of worldwide anti-terrorist measures, it’s more and more vital to conduct safety checks in public locations to detect hid objects carried on the human physique.

Earlier research have proved that deep learning is useful for detecting hid objects in passive terahertz (THz) images. Nevertheless, real-time labeling with superior accuracy and efficiency remains to be difficult.

A analysis group led by Prof. Fang Guangyou from the Aerospace Info Research Institute (AIR), Chinese language Academy of Sciences (CAS), has skilled and examined a promising detector primarily based on deep residual networks utilizing human picture information collected by passive terahertz gadgets. The proposed technique can be utilized for correct and real-time detection of hidden objects in terahertz photographs.

The examine was printed in Scientific Studies on July 15.

The analysis group changed the spine community of the Single Shot MultiBox Detector (SSD) algorithm with a extra consultant residual community to cut back the problem of community coaching. Aiming on the issues of repeated detection and missed detection of small targets, a function fusion-based terahertz picture goal detection algorithm was proposed.

Moreover, they launched a hybrid consideration mechanism in SSD to enhance the algorithm’s capability to accumulate object particulars and site data.

The analysis group additionally in contrast the proposed mannequin with different mainstream detection strategies on the terahertz human safety picture dataset. The outcomes confirmed that the proposed technique achieves improved detection accuracy as compared with the unique SSD algorithm when the pace is simply barely decreased.

The improved SSD algorithm addresses the problem of missed detection whereas additionally enhancing detection confidence. Subsequently, it might probably meet the real-time detection wants of safety inspection situations.


HERNet: A novel network for salient object detection in computer vision


Extra data:
Lu Cheng et al, Improved SSD community for quick hid object detection and recognition in passive terahertz safety photographs, Scientific Studies (2022). DOI: 10.1038/s41598-022-16208-0

Quotation:
Deep studying know-how allows quicker and extra correct terahertz safety inspection (2022, July 26)
retrieved 26 July 2022
from https://techxplore.com/information/2022-07-deep-technology-enables-faster-accurate.html

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