## 1 Introduction  
In recent years, the riotous events occurred frequently at crowd gathering places, such as public transport stations, resulted in many casualties and economic losses. The social impact is extremely bad, so the effective detection of contrabands hidden in clothing is important, and parcels have become an urgent need for social security. Traditional contraband detection is generally divided into baggage security and human security, and the most common way of baggage security is to use high-power X-ray scanners to penetrate the parcel layer for high-quality imaging of hidden contrabands [1]. However, excessive X-ray radiation may cause damage to the inspected personnel by ionisation and cannot be adapted to human security screening needs. Therefore, for the human body security screening, the current widespread use of metal security gates with hand-held metal detectors can effectively detect the metal objects carried by a person. However, metal detectors cannot obtain images for more information and cannot detect ceramic knives, lighters, and other non-metallic items. The low detection speeds of metal detectors for personal inspection will also cause people to become frustrated. To break the limitations of the traditional means of contraband detection, contraband detection based on terahertz (THz) imaging technology has received extensive attention of researchers. A THz wave is an electromagnetic wave with a frequency range between 0.1 THz and 10 THz, and a wavelength of 0.03-3 mm, which has the characteristics of low photonic energy and non-destructive detection [2]. THz waves can penetrate many commonly used non-polar materials, such as paper, textiles, plastic products, etc. THz imaging is a technique for imaging based on THz waves, and THz imaging technology has flourished to meet real-time high-resolution imaging [3,4]. Terahertz imaging technology is an effective complement to X-ray imaging technology because of its fast imaging speed, harmlessness to the human body, and strong penetration, and it has good potential for applications in the field of hazardous materials' detection [5,6]. The detection of contrabands in terahertz images often requires visual observation with low recognition efficiencies. Therefore, extracting features of targets from terahertz images and automatically and accurately identifying and locating various types of targets is the key to efficient detection. Traditional target detection methods for terahertz images include threshold segmentation methods [7], edge-based detection methods [8], and clustering-based detection methods [9]. However, the traditional terahertz image detection algorithms are not effective because terahertz images lack texture details, the target contour is not obvious, and it is more likely to produce misdetection and omission detection in complex background environments. In recent years, with the continuous development of deep learning technology, target detection algorithms based on convolutional neural networks (CNNs) have developed rapidly and have been widely used in various fields. The target detection task is mainly used for finding the target location in the image and classifying it. Deep learning-based target detection algorithms, instead of traditional manual feature selection, can be divided into two-stage target detection and single-stage target detection according to the detection process [10]. The two-stage target detection algorithms represented by Faster R-CNN [11] and Mask R-CNN [12] firstly carry out the generation of candidate regions, and then classify the candidate regions with convolutional neural networks. The two-stage target detection algorithm has high accuracy, but its detection speed is slow. The single-stage target detection algorithms represented by YOLO [13, 14, 15, 16] series and SSD [17] series do not need to generate the candidate region process, and directly locate and classify the target by deep convolutional neural network.