Multispectral Power Equipment Detection via Pixel-Based Model

The patent replaces the manual mechanical analysis system with an automated computer vision system. The detection model automatically processes infrared, ultraviolet, and visible light images to identify power equipment and their states, substituting human inspectors with an intelligent system that performs feature extraction, segmentation, and classification without manual intervention.

The detection system performs self-service by automatically completing the entire detection workflow including image acquisition, preprocessing, feature extraction, equipment identification, and state assessment. The system serves itself by maintaining and updating the detection models without requiring external expert intervention for each detection task.

The patent implements preliminary action by pre-training detection models on large datasets of power equipment images before actual detection tasks. The models are pre-trained to recognize various equipment types, structures, and defect patterns, enabling them to perform accurate detection without requiring real-time expert analysis. This preliminary preparation stores professional knowledge within the model parameters.

The system creates a digital copy of expert detection capabilities through trained neural network models. The models replicate the decision-making processes and pattern recognition skills of human experts by learning from annotated training data, effectively copying professional expertise into an automated system that can consistently apply the same detection standards without fatigue or subjectivity.