A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

Hyukzae Lee,Jihong Son,Woong Park,Jonghee Kim,Yongchan Park,Chanho Jung

doi:10.3390/app10144744

Abstract

The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the AFT. To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network’s anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.

Highlights

The fragmentation of projectiles or warheads leads to the outbreak of a large number of fragments with various masses and geometries [1]
In the first phase (i.e., convolutional neural network (CNN)-based detection) of our approach, we found the first penetrating fragments (FPFs) candidates
For the task of finding FPF candidates, we demonstrated that the CNN-based detection outperformed the image processing-based detection

Summary

Introduction

The fragmentation of projectiles or warheads leads to the outbreak of a large number of fragments with various masses and geometries [1]. Fragmentation is the procedure by which the wrapping of projectiles or warheads from a bomb, land mine, missile, etc. Warheads are explosive materials carried by a flight vehicle, like a shell, rocket, missile, or fighter, to destroy and disable targets. The objective of the arena fragmentation test (AFT) is to measure the warhead performance. High explosive (HE) warhead performance has been characterized based on fragmentation features, including data on the fragment mass, number and shape, initial fragment velocities, warhead case and explosive material performances, and spatial fragment distributions. The lethal efficiency of the HE warhead is a function of the fragment velocity, the geometrical shape

Objectives

Results

Conclusion