Multimodal Real-Time Anomaly Detection and Industrial Applications

TL;DR

This paper develops a multimodal, real-time anomaly detection system using synchronized video and audio, demonstrating significant accuracy and robustness improvements for industrial monitoring applications.

Contribution

The paper introduces an advanced multimodal system with multi-model audio ensembles, hybrid object detection, and cross-modal attention, enhancing real-time anomaly detection in industrial environments.

Findings

Achieves high accuracy in real-time industrial monitoring

Demonstrates robustness across various scenarios

Improves detection performance with multi-model fusion

Abstract

This paper presents the design, implementation, and evolution of a comprehensive multimodal room-monitoring system that integrates synchronized video and audio processing for real-time activity recognition and anomaly detection. We describe two iterations of the system: an initial lightweight implementation using YOLOv8, ByteTrack, and the Audio Spectrogram Transformer (AST), and an advanced version that incorporates multi-model audio ensembles, hybrid object detection, bidirectional cross-modal attention, and multi-method anomaly detection. The evolution demonstrates significant improvements in accuracy, robustness, and industrial applicability. The advanced system combines three audio models (AST, Wav2Vec2, and HuBERT) for comprehensive audio understanding, dual object detectors (YOLO and DETR) for improved accuracy, and sophisticated fusion mechanisms for enhanced cross-modal learning. Experimental evaluation shows the system's effectiveness in general monitoring scenarios as well as specialized industrial safety applications, achieving real-time performance on standard hardware while maintaining high accuracy.