基于多模态语义理解与强化学习的绝缘子缺陷多场景自适应检测方法OA

To address the poor robustness of traditional image processing methods and insufficient cross-scenario generalization of deep learning models for insulator defect detection on high-voltage transmission lines,a multi-scenario adaptive detection method based on multimodal semantic understanding and reinforcement learning is proposed.First,a contrastive language-image pre-training(CLIP)multimodal semantic encoder is employed to extract semantic features from both images and scene labels,enabling scene type identification via cosine similarity matching.Then,based on the identified scene type,a multimodal generative model is applied for targeted data augmentation,and a multi-scale YOLO detection cluster is constructed to accommodate object detection requirements under different scenarios.Finally,a reinforcement learning scheduling mechanism is introduced to dynamically select the optimal detection model and parameters according to real-time detection feedback,thereby forming a closed-loop adaptive optimization.Experimental results demonstrate that the proposed method maintains high detection accuracy and robustness in multiple complex scenarios,including rainy,foggy,backlit,and nighttime conditions,outperforming existing single-model approaches and exhibiting strong potential for engineering applications.