# Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods
## 2 Related Work
### Terahertz Image Detection  
Image object detection is a fundamental task in image processing. The task needs to judge not only what the object is but also the position of the object. In recent years, with the outbreak of deep learning, target detection technology in the field of machine vision has developed rapidly. Traditional manual feature design methods, such as HOG, SIFT and LBP [16; 17; 18; 19] can only achieve good results in specific scenes and cannot adapt to complex and diverse large-scale image data.The abstract features that can adequately describe objects are often ignored when designing features manually [20], and the designed network usually needs to be trained separately to conduct multi-level positioning. In recent years, the boom of deep learning based on a convolutional neural network (CNN) provides another idea for object recognition [21]. The target detection algorithm based on a convolution neural  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Class** & **Screwdriver** & **Blade** & **Knife** & **Scissors** & **Boardmarker** & **Mobile Phone** & **Wireless Mouse** & **Water Bottle** \\ \hline No. & 65 & 21 & 66 & 59 & 40 & 40 & 40 & 40 \\ Avg. bounding box & 108 px \(\times\) 84 px & 36 px \(\times\) 35 px & 89 px \(\times\) 75 px & 104 px \(\times\) 91 px & 78 px \(\times\) 68 px & 110 px \(\times\) 87 px & 70 px \(\times\) 75 px & 118 px \(\times\) 91 px \\ \hline \end{tabular}
\end{table}
Table 1: Original terahertz image data.  
Figure 4: Histogram distribution and scatter diagram of bounding boxes. The sizes of other categories are widely distributed and evenly distributed.  
network automatically extracts image features through the convolution layer, which has greatly improved the efficiency and accuracy. The earliest CNN-based target detection algorithm is the RCNN algorithm proposed by [PERSON] in 2015, which is followed by classic two-stage detection networks such as Fast RCNN and Mask RCNN [22; 23; 24; 25; 26]. Because the two-stage detection network consumes additional computing resources in the region proposal stage and the amount of model parameters is large, the overall detection speed of the two-stage detection network is slow. Researchers also proposed the one-stage detection network, which is represented by YOLO series [16; 27; 28; 29; 30]. The one-stage detection network directly uses the output of the convolution feature map for classification prediction and position fitting to reduce the amount of calculation. In addition, there are some anchor-free algorithms such as FCOS, CenterNet, etc.  
Although the above algorithms have achieved good index results in public datasets such as PASCAL VOC [31; 32] and COCO [33] datasets, there are still some problems in the application of terahertz image object detection. These problems are mainly caused by the characteristics of the terahertz dataset, namely: (1) image blur and (2) uneven distribution of the image histogram (as shown in Figure 4). The characteristics of these datasets will cause certain detection errors in the existing detection framework model for terahertz. Based on the YOLOv5s model, this paper redesigns the head structure of the detection model for terahertz datasets, adopts the BiFPN structure [34; 35] and realizes skip connection in convolution feature fusion, which can fuse richer image features than the original PANet [36; 37].  
The main contribution of this work is summarized as follows:  
1. Improving low resolution using BiFPN at the neck of YOLOv5 of the deep learning model.
2. Transfer learning is done using the fine-tuning process to the pre-training weight of the backbone for migration learning in our model.  
The remainder of this work is as follows. In Section 3, we advance our model encompassing the backbone and neck, whereas in Section 4, we present experimental work involving image processing, model comparison and model transfer learning. We conclude the paper in Section 5.