Advanced Digital Image and Signal Processing Techniques for Real-Time Anomaly Detection

Abstract

Real-time anomaly detection is a core requirement in industrial monitoring, surveillance, biomedical diagnostics and predictive maintenance, where unusual events must be identified within strict latency limits. This paper presents a unified framework that combines digital image processing (DIP) and digital signal processing (DSP) techniques for detecting anomalies in streaming visual and one-dimensional sensor data. The framework consists of modality-specific pre-processing, spatial and time–frequency feature extraction, feature-level fusion, unsupervised anomaly scoring and an adaptive decision threshold. Techniques reviewed and integrated include adaptive background modelling, optical flow, edge and texture analysis, the shorttime Fourier transform, wavelet decomposition, statistical indicators, autoencoder reconstruction error and isolation-based scoring. Real-time design considerations such as sliding-window processing, computational complexity and embedded deployment are discussed, and an evaluation protocol based on precision, recall, F1-score, ROC-AUC and detection latency is defined. The framework is demonstrated on reproducible synthetic vibration-signal and surface-image data; the signal pipeline achieved an F1-score of 0.949 with a per-window processing time of about 2.0 ms, and the image pipeline achieved a recall of 1.000 at about 1.6 ms per frame on a general-purpose processor. A qualitative comparison of methods and a set of design guidelines are provided to help practitioners select a suitable technique for a given application and hardware budget.