A multi-scenario adaptive detection method for insulator defects based on multimodal semantic understanding and reinforcement learning

GE Jingtian; LAI Zhichao; LU Feng; WANG Baojin; WANG Xiaojing

doi:10.19783/j.cnki.pspc.251270

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A multi-scenario adaptive detection method for insulator defects based on multimodal semantic understanding and reinforcement learning

更新时间：2026-09-23

- A multi-scenario adaptive detection method for insulator defects based on multimodal semantic understanding and reinforcement learning
- Power System Protection and Control Vol. 54, Issue 18, (2026)
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- DOI：10.19783/j.cnki.pspc.251270
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- Published：2026
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GE Jingtian, LAI Zhichao, LU Feng, et al. A multi-scenario adaptive detection method for insulator defects based on multimodal semantic understanding and reinforcement learning[J]. Power System Protection and Control, 2026, 54(18).
DOI：

GE Jingtian, LAI Zhichao, LU Feng, et al. A multi-scenario adaptive detection method for insulator defects based on multimodal semantic understanding and reinforcement learning[J]. Power System Protection and Control, 2026, 54(18). DOI： 10.19783/j.cnki.pspc.251270.

摘要

针对高压输电线路绝缘子缺陷检测中传统方法鲁棒性差，深度学习模型跨场景泛化性能不足的问题，提出一种基于多模态语义理解与强化学习的多场景自适应检测方法。首先，利用对比语言-图像预训练(contrastive language-image pre-training

CLIP)多模态语义编码器提取图像与场景标签的语义特征，通过余弦相似度匹配实现场景类型识别。然后，基于场景识别结果，采用多模态生成模型对训练数据进行针对性增强，并构建多尺度YOLO检测集群以适应不同场景下目标检测的需求。最后，引入强化学习调度机制，根据实时检测反馈动态选择最优检测模型与参数，形成闭环自适应优化。实验结果表明，该方法在雨天、雾霾、逆光、夜间等多种复杂场景下均能保持高检测精度与鲁棒性，优于现有单一模型方法，具有良好的工程应用前景。

Abstract

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.

关键词

Keywords

references

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