Deep-learning-based method for concealed object detection in terahertz (THz) images

Terahertz (THz) technology has become a new trend in various fields due to its high penetration and harmlessness towards human body and objects. The object detection of concealed and hidden objects based on THz images is of great significance for ensuring public safety. However, the poor quality of original THz images leads to insufficient accuracy in target detection. Therefore, it is necessary to preprocess the images before performing object detection. In this work, in order to investigate the impact of different pre-processing methods on object detection using images, we adopt two methods, namely non-local mean (NLM) filtering and histogram equalization (HE). After pre-processing, YOLOv7 algorithm is used to perform object detection based on the preprocessed THz images. The experimental results show that YOLOv7 achieves highest recognition accuracy on NLM filtered THz images. The experimental results presented in this work provide a reference to select image processing method for performing concealed object detection based on THz images.