基于预训练网络的重力波光谱图像异常检测及定位技术

doi:10.11896/jsjkx.250400117

Abstract

Abstract: To addresses the issues of indistinct features and uneven distribution in gravitational wave spectral image data,which often lead to high error rates in anomaly detection.This paper proposes an anomaly detection method for gravitational wave spectral images based on a pre-trained network.This method analyzes image features at both the image level and pixel level,employing preprocessing techniques to enhance the key features of the images,thereby more accurately capturing useful feature information within the images.The intermediate layers of an ImageNet pre-trained network are utilized for feature extraction,and a core-set subsampling mechanism is applied to compress the feature memory bank,reducing inference analysis time.Finally,the nearest neighbor algorithm is used to calculate the anomaly scores of image pixels,enabling the assessment of the overall anomaly degree of the image and the identification of anomalous regions.Experimental results demonstrate that this method can effectively analyze features of gravitational wave spectral images at both image and pixel levels,utilize image features for anomaly detection,and accurately identify anomalous regions in gravitational wave spectral images.The AUROC metrics for anomaly discrimination and localization reach 98.73% and 95.19%.

Key words: Anomaly detection, Core subset sampling, Unsupervised learning, Pretrained model, Gravity wave spectral image

CLC Number:

TP391

HUANG Siyang, YAO Ye, ZHU Yian, HAI Duo, XIONG Zhihai. Anomaly Detection and Localization Technology for Gravity Wave Spectral Images Based onPre-trained Networks[J].Computer Science, 2026, 53(5): 193-206.

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Anomaly Detection and Localization Technology for Gravity Wave Spectral Images Based onPre-trained Networks

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