Chrome Extension
WeChat Mini Program
Use on ChatGLM

Design and Proposal of a Database for Firearms Detection

Advances in Intelligent Systems and Computing(2019)

Speech Technology Group

Cited 4|Views10
Abstract
Closed circuit television (CCTV) surveillance systems that implement monitoring operators have multiple human limitations, these systems usually don’t provide an immediate response in different situations of danger like an armed robbery. To address this security gap, a firearms detection system has been developed through convolutional neural networks (CNNs). For its development a large database of images is necessary. This article presents the creation and characteristics of this database, which is made up of 247,576 images obtained from the web. This article addresses the application of different techniques for the creation of new images from the initial ones to increase the database, obtaining up to 22.7% relative improvement in the accuracy of the network after increasing the database. The database is structured into two classes. The first class is made up of people that have a gun and the second class of people not carrying a gun. The use of this database in the development of the detection system obtained up to 90% in “Precision” and “Recall” metrics in a convolutional neural network configuration based on “VGG net”, through the use of grayscale images.
More
Translated text
Key words
Convolutional neural network,Database,Detection,Firearm
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了一种基于卷积神经网络(CNNs)的枪支检测系统,并创建了一个包含247,576张图片的大型数据库,通过数据增强技术提高了检测精度,实现了在枪支检测任务中的高准确性和召回率。

方法】:作者使用卷积神经网络,特别是基于VGG net的配置,来开发枪支检测系统,并应用了不同的图像生成技术以增强数据库。

实验】:实验使用了一个由247,576张网络图片构成的数据库,分为持枪者和非持枪者两类。通过数据增强,数据库规模扩大,网络准确度相对提高了22.7%。该数据库在VGG net配置的卷积神经网络中应用,对灰度图像的处理达到了90%的精确度和召回率。