  Concealed object detection in Terahertz imaging is an urgent need for public
security and counter-terrorism. In this paper, we provide a public dataset for
evaluating multi-object detection algorithms in active Terahertz imaging
resolution 5 mm by 5 mm. To the best of our knowledge, this is the first public
Terahertz imaging dataset prepared to evaluate object detection algorithms.
Object detection on this dataset is much more difficult than on those standard
public object detection datasets due to its inferior imaging quality. Facing
the problem of imbalanced samples in object detection and hard training
samples, we evaluate four popular detectors: YOLOv3, YOLOv4, FRCN-OHEM, and
RetinaNet on this dataset. Experimental results indicate that the RetinaNet
achieves the highest mAP. In addition, we demonstrate that hiding objects in
different parts of the human body affect detection accuracy. The dataset is
available at https://github.com/LingLIx/THz_Dataset.
