<scp>MVFE</scp> ‐ <scp>MC</scp> : Multi‐View Feature Extraction and Multimodal Classifier for Concrete Defect Image Classification

ABSTRACT In recent years, deep learning‐based vision technologies have gradually become a research hotspot for concrete defect classification. However, existing concrete defect classification datasets generally suffer from issues such as blurry boundaries, labeling errors, limited data sources and a lack of diversity in defect types, which are insufficient to meet practical application requirements. At the same time, current methods still face challenges such as data scarcity, single‐modal information and high computational resource demands. To address these issues, this paper proposes a novel concrete defect classification dataset: ConDef, and introduces an innovative multi‐view feature enhancement method: MVFE‐MC. This method combines large language models (LLMs) with deep learning techniques to achieve high‐precision concrete defect classification in low‐resource and few‐shot environments. The ConDef dataset is constructed through multiple channels to ensure data diversity and high quality, and a triple‐validation mechanism is adopted to ensure labeling accuracy. The MVFE‐MC method introduces a Multi‐View Feature Module and a Multimodal Classifier Module, effectively integrating visual and textual information to enhance the model's ability and robustness in fine‐grained defect recognition. Experimental results show that MVFE‐MC achieves state‐of‐the‐art performance on two concrete defect classification datasets, Concrete Crack Image and ConDef, validating the effectiveness and superiority of the proposed method.