Terahertz detection and recognition of suspicious objects hidden in the human body based on DeepLabV3+ deep learning model

Abstract

The passive terahertz imaging human body security technology has begun to be applied in dense pedestrian flow security fields such as subways and public venues. However, passive terahertz imaging systems directly generate terahertz images, which have problems such as low signal-to-noise ratio and poor resolution. For the identification of suspicious objects hidden under human clothing, the naked eye observation method by security personnel is difficult to distinguish suspicious objects, with a high error rate and slow speed. To balance the accuracy and speed of detecting suspicious objects hidden under human clothing in security scenarios, taking into account terahertz image quality and target detection accuracy, a DeepLabV3+ deep learning model is used to detect targets, achieving object detection and recognition based on passive terahertz imaging human security systems.