领域知识引导的视觉语言模型配电线路金具锈蚀检测方法OA

To address the issues such as difficulty in defect representation,insufficient semantic information,and am-biguous class boundaries in corrosion detection of distribution-line fittings,a domain knowledge-guided vision-language detection method is proposed.First,a low-contrast feature enhancement module is designed to enhance textures,bounda-ries,and key channel responses of low-contrast corrosion regions through multi-scale directional extraction,boundary-guided modulation,and channel adaptive recalibration.Second,a knowledge-guided representation module is constructed to transform distribution-line domain knowledge into textual prompts and to dynamically optimize text em-beddings with a vision-conditioned prompt adapter,improving the model's semantic understanding of corrosion patterns and fitting states.Finally,a cross-modal semantic alignment strategy is introduced to constrain visual-semantic consisten-cy through region-text contrastive classification loss and semantic alignment regularization,enhancing discrimination of similar categories.The experimental results show that the proposed method achieves 87.1％mean average precision(mAP50)on a self-built defect dataset of distribution-line fittings.In open-semantic detection and zero-shot generalization experiments,the mAP50 values reach 81.2％and 44.2％,respectively,with an inference speed of 34.9 frames per second.The proposed method can achieve a balance among detection accuracy,open-category generalization and inference effi-ciency,providing a technical support for defect detection of distribution-line fittings.