文章基本信息

标题：Target Detection and Classification Performance Enhancement using Super-Resolution Infrared Videos
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作者：Chiman Kwan ; David Gribben ; Bence Budavari 等
期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2021
卷号：12
期号：2
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Long range infrared videos such as the Defense Systems Information Analysis Center (DSIAC) videos usually do not have high resolution. In recent years, there are significant advancement in video super-resolution algorithms. Here, we summarize our study on the use of super-resolution videos for target detection and classification. We observed that super-resolution videos can significantly improve the detection and classification performance. For example, for 3000 m range videos, we were able to improve the average precision of target detection from 11% (without super-resolution) to 44% (with 4x super-resolution) and the overall accuracy of target classification from 10% (without super-resolution) to 44% (with 2x superresolution).
关键词：Deep learning;mid-wave infrared (MWIR) videos;target detection and classification;contrast enhancement;YOLO;ResNet