Intelligent Method for Detecting Equipment Surface Damage Based on Multi-Feature Fusion

The intelligent detection of surface damage is crucial for ensuring safe and stable equipment operation. This study addressed the low efficiency, strong subjectivity, and inadequate representation capabilities of traditional single-feature-based damage identification methods by proposing an intelligent equipment surface damage classification method employing multi-feature fusion. The innovations of this method comprise the construction of a seven-dimensional model integrating brightness, texture, frequency domain, edge, and morphological features to comprehensively characterize the visual characteristics of six typical types of damage (fine crazing, inclusions, patches, pitted surfaces, rolled-in oxide scale, and scratches) and the application of a Bayesian probabilistic classification model based on adaptive kernel density estimation to identify damage type and determine the associated confidence. Experimental results obtained using a self-assembled dataset comprising 1619 training samples and 180 test samples indicated that the proposed method achieved a comprehensive accuracy of 97.77%, significantly outperforming traditional deep neural networks and Ant Forest algorithms. Thus, the proposed method provides an effective solution for the intelligent and efficient detection of equipment surface damage.