# A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test
## 2 Deep Learning-Based Fragment Detection  
### Overview  
The object detection technique is a technique to detect the position of an object in an image in the form of a bounding box and to classify the object categories. Recently, with the development of artificial intelligence, high-performance object detection techniques using convolutional neural networks (CNNs) have been proposed. Such object detection techniques are largely divided into two groups. One is a single step detection technique, and the other is a region-based two-step detection technique. Representative examples of single-stage techniques are single shot detector (SSD) [25] and you only look once (YOLO) [26] detectors. The single-stage technique first divides the feature point map obtained from the CNN feature point extractor into a grid cell of a predetermined interval. Then, by using the features in each grid, information about the bounding box indicating the position of the object and the type of the object are simultaneously predicted. This single step technique is characterized by a fast reasoning speed. However, there is a disadvantage in that small objects, such as warhead fragments, cannot be detected. A representative example of a two-step region-based technique is the Faster R-CNN [27] detector. The Faster R-CNN consists of a region proposal network (RPN) and a Fast R-CNN [28] detector. Both networks share the same CNN feature extractor. First, the RPN searches a bounding box indicating the position of an object at each pixel position from the extracted feature point map and detects only the bounding box corresponding to the object. Next, the Fast R-CNN determines the type of the object using the feature points included in the detected bounding box. This area-based technique is effective for reflecting the approximate size of an object to the detector during the learning process. Due to these characteristics, it shows a higher performance in detecting small objects, such as warhead fragments, compared to the single-stage technique using the feature map as the grid unit. Therefore, this study effectively detected warhead fragment data in ATFs by using Faster R-CNN suitable for small object detection. Figure 6 shows example input warhead fragment images where the green box represents the first penetrating warhead fragment contained within the images, and the blue box represents the flame that occurs at that location after fragment penetration. The green and blue bounding boxes represent the FPF (ground truth) and flame, respectively. The red bounding box indicates the FPF detected by our proposed two-step approach, which consists of (1) CNN-based detection and (2) temporal filtering. Naturally, the ground truth bounding boxes can be overlapped, as the experts made the ground truth boxes. The flames can, of course, be overlapped as well. There are also overlaps between the ground truth FPFs and flames. For example, the green and blue boxes can be overlapped. In addition, a green (blue) box and the other green (blue) box can be overlapped. From a physical point of view, a red box (whose ground truth is a green box) overlapped with a mask (which corresponds to a blue box) cannot be detected as a FPF when there is even a slight overlap between the red box and the mask. Note that the appearance and shape of FPFs are \"inconsistent\". Some FPFs are very bright (see 1 and 2 in Figure 5(b)). On the other hand, there are darker FPFs (see 1 in Figure 5(b)). The appearance and shape of flames are also \"inconsistent\". There are brighter and darker flames. For this reason, the FPFs and flames have \"very similar\" appearances and shapes (see 1, 2, and 1 in Figure 5(b)). Note also that the overlap between green and red boxes indicates the correct detection (see 1, 2, and 1 in Figure 5(b)). There could be false negatives (see 1 and 2 in Figure 5(b)). There could also be false positives (see 1 in Figure 5(b)). The 1 in Figure 5(b) indicates the noise. The false negatives indicate real FPFs that are incorrectly determined as non-FPFs by the algorithm.