论文标题
使用深度学习的弹药组件分类
Ammunition Component Classification Using Deep Learning
论文作者
论文摘要
弹药废料检查是回收弹药金属废料的过程中的重要步骤。大多数弹药由许多组件组成，包括盒子，底漆，粉末和弹丸。包含能量学的弹药废料被认为是潜在的危险，应在回收过程之前分离。手动检查每块废料都是乏味且耗时的。 We have gathered a dataset of ammunition components with the goal of applying artificial intelligence for classifying safe and unsafe scrap pieces automatically.首先，通过弹药的视觉和X射线图像手动创建两个培训数据集。 Second, the x-ray dataset is augmented using the spatial transforms of histogram equalization, averaging, sharpening, power law, and Gaussian blurring in order to compensate for the lack of sufficient training data. Lastly, the representative YOLOv4 object detection method is applied to detect the ammo components and classify the scrap pieces into safe and unsafe classes, respectively.训练有素的模型针对看不见的数据进行了测试，以评估应用方法的性能。实验证明了使用深度学习的弹药组件检测和分类的可行性。 The datasets and the pre-trained models are available at https://github.com/hadi-ghnd/Scrap-Classification.
Ammunition scrap inspection is an essential step in the process of recycling ammunition metal scrap. Most ammunition is composed of a number of components, including case, primer, powder, and projectile. Ammo scrap containing energetics is considered to be potentially dangerous and should be separated before the recycling process. Manually inspecting each piece of scrap is tedious and time-consuming. We have gathered a dataset of ammunition components with the goal of applying artificial intelligence for classifying safe and unsafe scrap pieces automatically. First, two training datasets are manually created from visual and x-ray images of ammo. Second, the x-ray dataset is augmented using the spatial transforms of histogram equalization, averaging, sharpening, power law, and Gaussian blurring in order to compensate for the lack of sufficient training data. Lastly, the representative YOLOv4 object detection method is applied to detect the ammo components and classify the scrap pieces into safe and unsafe classes, respectively. The trained models are tested against unseen data in order to evaluate the performance of the applied method. The experiments demonstrate the feasibility of ammo component detection and classification using deep learning. The datasets and the pre-trained models are available at https://github.com/hadi-ghnd/Scrap-Classification.
