# A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test
Recently, machine learning technologies have been extensively used in diagnosis, sensing, monitoring, and measurement applications [6; 7; 8; 9; 10; 11]. In addition, in such applications, image processing and computer vision technologies have been widely employed [12; 13; 14; 15; 16; 17]. Studies on measuring fragment data based on images have also been conducted. [PERSON] [18] and [PERSON] [19] analyzed the performance of warheads and the flight of fragments using X-rays. [PERSON] [20] studied the techniques using two optical devices to measure the characteristics of the fragment flying. However, these studies have the disadvantage of being able to analyze only short-term phenomena, such as the initial stage of detonation. In order to solve this problem, a long-time and multi-fragment measurement algorithm [21] using four stereo images was proposed however, this algorithm has slow measurement speed and overlapping problems, which are not suitable for warhead tests requiring high speed and multi-fragment measurement. In recent years, we introduced an image processing-based fragment detection approach by modeling an appropriate fragment feature [22]. In this paper, to overcome the limitations of the existing AFT measurement system, we propose a warhead fragment data measurement system using an object detection technique, which is one of the  
Figure 1: An arena fragmentation test environment. most important deep learning-based computer vision technologies [23; 24]. To construct the proposed warhead fragment data measurement system, we first captured the warhead fragments that penetrated the steel plate using a high speed camera sensor as shown in Figure 2, and then designed a deep learning-based fragment detector to detect only the first penetrated fragments from the captured images. The proposed method consists of (1) deep learning-based detection of warhead fragments from the captured warhead fragment images and (2) finding only the first penetrated warhead fragments using a temporal filtering scheme [22]. We will show that the proposed method outperformed the previous image processing-based fragment detection method [22]. More specifically, in our previous image processing-based fragment detection method [22], our aim was to detect bright regions from the dark background. The bright region was carefully modeled to detect first penetrating fragments (FPFs). Although such an approach worked well, in this paper we will demonstrate that the use of convolutional neural network (CNN)-based detection in the first phase outperforms the use of image processing-based detection in the first phase. Figure 3 shows an example representing the difference between the first penetrating fragment (FPF) and flame. In Figure 3, the green and blue bounding boxes indicate the FPFs and flames, respectively. To make it more clear, the shape and appearance (yellow circles) of the fragments have not changed. In practice, the shape and appearance change after the first penetration. Note here that the ultimate goal of AFT is to detect \"first\" penetrating fragments (FPFs). In other words, the AFT has \"no interest\" in detecting the subsequent penetrating fragments (such as the second and third penetrating fragments) that penetrate the same area. Furthermore, in reality, the repenetration (e.g., the second penetration) for the same area is \"very unlikely\" to occur. This is because the explosion of a warhead has the property of bursting radially and thus fragments pass different location. Note also that, after the first penetration, the flame occurs in the penetrated region and its surrounding nearby area. In the first phase (i.e., CNN-based detection) of our approach, we found the FPF candidates. The FPF candidates included not only the true FPFs but also the flames. Then, in the second phase (i.e., temporal filtering), the flames were removed. The first and second phases were very important. The shapes as well as appearances of the FPFs and flames were very similar.