@inproceedings{cac805849b3c448ab2a1f8c9883a4bca,
title = "Assault Rifle Detection and Identification Based on Convolutional Neural Network YOLOv3",
abstract = "In modern war, armies from different countries have different weapons for infantry. Therefore, identifying the weapons held by infantry helps to determine their affiliations. In this paper, we have built our dataset of different types of assault rifles from photos. The dataset was trained using the convolutional neural network YOLOv3. Several different real videos were used to verify the YOLOv3 training results. The experiments show that the model can detect and identify assault rifles in different scenes. It proves to work with a good accuracy and can be used in real time on battlefield.",
keywords = "YOLOv3, deep learning, object detection, real-time detection",
author = "Yunfei Ma and Huimin Chen and Jian Huo",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd World Symposium on Artificial Intelligence, WSAI 2021 ; Conference date: 18-06-2021 Through 20-06-2021",
year = "2021",
month = jun,
day = "18",
doi = "10.1109/WSAI51899.2021.9486333",
language = "English",
series = "2021 3rd World Symposium on Artificial Intelligence, WSAI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2021 3rd World Symposium on Artificial Intelligence, WSAI 2021",
address = "United States",
}