# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
###### Abstract  
Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments.  
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pixel. Traditional object recognition often follows the detection-first regime. Unfortunately, the imaging quality mentioned above leads to poor detection. As far as we know, there is no public terahertz dataset for multi-target detection. For both of these reasons, multi-object detection techniques for terahertz images are not well developed. Our work focuses on this problem in the hope of advancing the task.  
In this paper, we provide an active terahertz imaging dataset for multi-object detection. The state-of-the-art deep-learning-based object detectors YOLOv3 [8], YOLOv4 [9], FRCN-OHEM [10] and RetinaNet [11] are evaluated on the dataset. Due to the high sample similarity, poor imaging quality, and small objects of terahertz images, general detectors do not perform well on this dataset. In order to detect smaller objects, we extended RetinaNet by embedding low-level features. Aiming at solving the problem of unbalanced training samples, we proposed a new Hard Example Mining (HEM) approach for the multi-object detection of terahertz images. Focal loss [11] and HEM are discussed and tested in this paper. We compare in detail the parameters that affect the image detection metrics and give a method for selecting the optimal threshold.  
The contributions of this paper are three-fold:  
* Provide an active terahertz imaging dataset for concealed multi-object detection. To our knowledge, there is no public dataset in terahertz imaging to evaluate multi-object detection algorithm. We provide an active terahertz imaging dataset for multi-object detection with 3157 image samples with 1347 concealed objects.
* An image-based Hard Example Mining scheme based on RetinaNet is designed, and four state-of-the-art object detectors are evaluated on this dataset. The experiment indicates that HEM further improves the performance of the RetinaNet.
* The experiment indicates that hiding objects in different parts of the human body affect detection accuracy. The parameters affecting the single-image detection metrics are summarized and analyzed in the experiments.  
The remainder of this paper is organized as follows: we discuss the related work in Section 2. The details of our proposed terahertz dataset are described in Section 3. We formulate the problem and describe the proposed method in detail in Section 4. The experimental results are presented and discussed in Section 5, and the conclusions and future work are presented in Section 6.