COMPUTATIONAL METHOD OF BINARY SEMANTIC SEGMENTATION OF BUILDING STRUCTURE DEFECTS BASED ON CONVOLUTIONAL NEURAL NETWORKS
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Abstract
The problem of automated diagnostics of defects in building structures is due to the high labor intensity and subjectivity of visual inspections. The paper presents a prototype computer vision system based on an architecture featuring a lightweight feature extraction encoder, designed for the binary semantic segmentation of cracks in building surface images. The new proposal is to adapt the specified architecture to the problem of detecting thin extended objects on a non-uniform background using a combined loss function that combines binary cross-entropy and the Dice coefficient. The paper provides the mathematical formulation of the problem, a formal description of the modified optimization criterion, a data preprocessing methodology that accounts for JPEG compression artifacts, and the results of computational experiments. Evaluated on an independent test set, the resulting metrics—specifically the Dice coefficient and Jaccard index (IoU)—validate the effectiveness of the proposed approach and outline directions for further technological improvements.
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