Multimodal Real-Time Anomaly Detection and Industrial Applications

2025-11-23

AI Summary

To address low accuracy and poor robustness in real-time multimodal anomaly detection caused by audio-video asynchrony and modality mismatch in industrial settings, this paper proposes a unified multimodal fusion framework. It introduces a bidirectional cross-modal attention mechanism for fine-grained alignment between video (YOLOv8/DETR + ByteTrack) and audio (AST/Wav2Vec2/HuBERT) streams, and integrates hybrid object detection with multi-strategy anomaly discrimination to enable synchronous streaming inference. Evaluated on general surveillance and industrial safety benchmarks, the system achieves >25 FPS on standard GPUs, with improvements of +4.2% mAP, +7.8% anomaly detection rate, and −12.3% false positive rate. Key contributions include: (i) the first end-to-end audio-visual synchronized anomaly detection system designed specifically for industrial deployment; and (ii) an extensible cross-modal attention architecture coupled with a lightweight audio integration scheme.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Developing real-time multimodal anomaly detection using synchronized video and audio processing
• Improving accuracy and robustness through cross-modal attention and multi-model ensembles
• Creating industrial safety applications with real-time performance on standard hardware

Innovation

• Integrates synchronized video and audio processing
• Uses multi-model audio ensembles and hybrid detectors
• Implements bidirectional cross-modal attention mechanisms