3 results about "Background distribution" patented technology

This invention relates to the field of defect detection technology, specifically to a PCB defect detection method for industrial incremental scenarios. It includes a feature fusion method based on a lightweight segmentation strategy that combines random pruning with separate processing of strong and weak features. This strategy simplifies redundant computation by using a feature selector, and differentiates and recombines defect features of varying saliency during the feature fusion stage. This significantly improves inference speed while maintaining the precision of segmentation, solving the problems of high computational cost and difficulty in capturing minute defects in existing methods. The method also includes an incremental learning approach employing a background classifier adaptation mechanism and local semantic distillation. Class-specific regularization and spatially weighted logical alignment distillation work synergistically. By dynamically calibrating the background prediction logic and constructing a pixel-level semantic relevance matrix, it achieves deep alignment between new and old knowledge and background distribution. This effectively solves the catastrophic forgetting problem caused by the evolution of PCB background texture in existing incremental learning methods, significantly enhancing detection stability and the adaptability of the enhanced model.

The application discloses a kind of intelligent behavior detection method and system based on motion feature enhancement, construct based on adaptive mixture Gaussian model, the Mahalanobis distance of pixel point and background distribution is calculated and model weight is dynamically updated, realize the accurate decoupling and separation of foreground motion region in video frame;Then, the foreground mask obtained initially is based on the connected domain analysis of area threshold to filter out discrete noise, and the morphological closing operation of structure element adaptation is used to fill target internal cavity and repair edge fracture, to generate the high signal-to-noise ratio of motion region binary graph;Again, the purified binary graph is mapped as high visual saliency color heat map, it is weighted linearly fused with original RGB video frame, the fusion feature map is used to guide deep learning model to pay attention to motion region, and through double threshold detection strategy, the perception and identification ability of system to small target and hidden uncivilized behavior under complex dynamic scene is greatly improved.

The present application relates to the technical field of carbon footprint uncertainty analysis, and particularly relates to a carbon footprint uncertainty calculation method and electronic equipment, the method comprising: automatically generating a plurality of rules from unstructured text comprising carbon footprint historical data; fuzzy weighted synthesis of the plurality of rules in a scenario to generate a credibility distribution; extracting a global range and a scenario range according to a credibility threshold, and truncating the probability density function of the obtained truncated normal distribution on the global range to obtain a background distribution; equally dividing the scenario range into three scenario sub-intervals, and calculating the scenario weights using the background distribution; conditionally truncating sampling for each scenario sub-interval, and independently running Monte Carlo simulation to output the corresponding mean and variance; and weighting and synthesizing the Monte Carlo simulation results of each scenario through the corresponding scenario weights to obtain the uncertainty of the carbon footprint. Compared with the prior art, the present application has the advantages of efficient and accurate analysis of carbon footprint uncertainty, etc.