Neural Network Feature Map Interior Detection

The patent divides the detection task into multiple independent feature maps, each capturing different aspects of the image data. By segmenting the feature extraction process into parallel maps that can be independently compared against reference values, the system achieves both high detection accuracy through multiple specialized features and robustness by not relying on any single feature map that might be affected by lighting or environmental changes.

The patent extends the comparison from single feature values to multi-dimensional feature spaces by comparing feature maps against reference feature maps. This dimensional expansion allows the system to consider multiple characteristics simultaneously (edges, textures, colors, shapes) and their relationships, improving detection accuracy while the reference comparisons provide stability against environmental variations.

The patent performs preliminary action by pre-computing and storing reference feature maps during a training phase. These reference maps capture the normal state of the interior across various conditions. During operation, the system only needs to compare current feature maps against these pre-established references, significantly reducing computational complexity while maintaining high reliability in false detection reduction.

The patent creates copies of reference feature maps that represent the normal interior state under various conditions. By comparing current feature maps against these copied references rather than performing complex real-time analysis, the system achieves robust false detection reduction with reduced computational burden. The copies serve as templates for efficient comparison.

The patent replaces the mechanical system of manual visual inspection by an operator with an automated image processing system using neural networks. The system captures images, extracts features, compares them against references, and automatically detects objects without human intervention. This substitution eliminates time consumption and labor requirements while maintaining or improving detection precision through consistent, fatigue-free automated analysis.

The patent implements self-service by enabling the system to automatically perform the entire inspection process without human operators. The neural network system independently captures images, processes features, compares against references, identifies deviations, and generates detection results. This self-service capability eliminates dependency on manual inspection while achieving reliable object detection.