AI Visual Anomaly Detection Server for Industrial Monitoring

The patent replaces conventional numerical-based monitoring systems with an AI-driven visual analysis system. The server uses machine learning models to process images from multiple cameras, automatically detecting visual anomalies such as leaks, spills, and equipment defects that numerical sensors cannot identify. This substitution enables precise visual anomaly detection while maintaining system integration through standardized communication protocols.

The patent introduces an AI-powered server as an intermediary between conventional monitoring systems and users. This server receives data from existing sensors and cameras, processes visual information using trained models, and generates actionable insights. The intermediary layer enables visual anomaly detection without requiring complete system replacement, thus managing complexity while improving detection capability.

The patent implements pre-trained AI models that have been trained offline on extensive datasets of normal and anomalous industrial scenes. During operation, the models perform inference rather than full training, significantly reducing computational energy requirements. The system maintains multiple trained models for different anomaly types, enabling accurate detection without real-time training computations.

The patent divides the visual monitoring task into specialized AI models for different anomaly types (leaks, spills, equipment defects, safety violations). Each model is optimized for specific detection tasks, reducing the computational burden compared to a single comprehensive model. The segmentation allows parallel processing of different camera feeds and anomaly types, improving efficiency.

The patent extracts only the most critical visual anomaly information and presents it in a prioritized format. The system identifies and highlights the most severe anomalies first, providing detailed images and locations for these critical issues while summarizing less severe anomalies. This extraction approach ensures operators receive complete anomaly information without being overwhelmed by all details simultaneously.

The patent provides different levels of information detail in different interface areas. Critical anomalies receive prominent display with full images and detailed descriptions, while less severe anomalies are shown in summary formats. The system adapts the level of detail based on anomaly severity and operator needs, ensuring important information is easily accessible while maintaining overall information completeness.