Open AccessResearch on Intrusion Detection and Target Recognition System Based on Deep Learning
Author(s) -
Xianwei Hu,
Tie Li,
WU Zong-zhi,
Xuan Gao
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/646/1/012055
Subject(s) - intrusion detection system , computer science , artificial intelligence , feature extraction , pattern recognition (psychology) , artificial neural network , process (computing) , feature (linguistics) , deep learning , computer vision , philosophy , linguistics , operating system
Intrusion target detection and recognition are of great significance to security protection of oil and gas fields. An intrusion detection system is built with the integration of infrared image acquisition module, infrared image processing module, moving target detection module and recognition module. Traditional target recognition algorithm highly relies on manual design feature extraction algorithm, which requires designer to have adequate prior knowledge, and cannot avoid the influence of subjective factors of people. Intrusion detection and target recognition system are proposed based on deep learning, which uses neural network algorithm. Deep learning model is built through feature extraction and training of acquired images of intrusion objects, and thus subsequent invasion objects are detected and recognized. Intrusion detection is achieved through simulation of human brain, which boasts of more intelligent recognition process and more accurate recognition results compared with traditional recognition method. According to applications in real scenario, the system proposed has better detection and recognition results and great practical value.