Intelligent analysis and processing of UAV-captured images by using computer vision has become the mainstream approach for power transmission line inspection. As the most widely used connection components in transmission lines，bolts play a critical role in structural integrity. However，under complex environmental conditions，bolts are prone to defects，such as lost nuts and pins，which may pose serious threats to the safe and stable operation of transmission systems. Therefore，bolt defect classification via computer vision techniques is essential to ensure transmission reliability. Vision-language models （VLMs） have high potential in bolt defect classification tasks where visual information is limited. However，vision-language pretraining approaches that are based on image-text contrastive learning typically require large-scale datasets and diverse textual descriptions. For instance，general-domain multimodal datasets often comprise millions of image-text pairs，and bolt defect datasets are characterized by small-scale data and limited textual descriptions. As a result，contrastive learning approaches cannot be directly applied to such tasks. To expand the applicability of VLMs in bolt defect classification，this study proposes a transmission line bolt defect classification method that integrates vision-language feature alignment and textual decoupling （VL-Bolt）.

We analyze the alignment behavior of image-text pairs in a training batch and identify a key limitation of standard image-text contrastive learning： intraclass semantic overlap. When multiple samples from the same class are present in a batch，image-text contrastive learning incorrectly treats identical class descriptions as negative pairs，hindering effective alignment between images and their textual counterparts. This issue is particularly pronounced in small-scale bolt defect datasets with limited textual diversity. Then，we adopt the CLIP ViT-B/32 model as the backbone. However，because of the lack of power domain knowledge in CLIP’s pretraining and the high similarity between different bolt defect features，its performance in fine-grained classification is constrained. To address this issue，we introduce a text feature decoupling （TFD） loss，which explicitly disentangles textual features of different defect categories in the feature space. Building upon the decoupled textual features，we further design a text-anchor-guided visual feature alignment （TA-VFA） strategy tailored to bolt defect classification. In TA-VFA，decoupled text features serve as anchors to guide the alignment of their corresponding image features，facilitating the effective separation of image representations across different defect categories. This architecture inherently resolves the intraclass semantic overlap issue，making it suitable for the bolt defect classification task. Moreover，considering the limited scale of the bolt defect dataset，we propose a progressive fine-tuning strategy for the image encoder to mitigate overfitting and improve generalization. Specifically，only Transformer layer 11 is unfrozen during early training，and additional layers are gradually unfrozen at the late stages. This scheme enables the model to prioritize learning high-level semantic features relevant to bolt defects while retaining low-level structural information and the generalization ability gained during pretraining.

In comparative experiments，VL-Bolt achieves a classification accuracy of 92.2% on the bolt defect dataset，surpassing the baseline by 3.9% and outperforming multiple ImageNet-pretrained vision models and four recent bolt defect classification models fine-tuned on bolt datasets. Compared with the multimodal MUCO-BD （multi-modal contrastive learning for bolt defect classification） model，the proposed VL-Bolt model has a slightly lower classification accuracy； however，it has approximately 29.9% fewer trainable parameters. Moreover，when the VL-Bolt model is pretrained with domain-specific knowledge with the same configuration as MUCO-BD，its performance surpasses that of MUCO-BD，demonstrating the superior adaptability of the proposed approach. We also compare our transfer strategy with four recently proposed transfer learning methods. Results demonstrate that VL-Bolt achieves superior accuracy and generalization performance. From the perspectives of algorithm principle and structure，the main reason for the poor classification performance of CLIP-Adapter，CoOp （context optimization），CoCoOp （conditional context optimization），and Tip-Adapter models is the excessively high interclass similarity of bolt defects. Compared with WiSE-FT （ensembling the weights of the zero-shot and fine-tuned models），a transfer method designed to enhance model generalization，VL-Bolt outperforms it by 2.2%，demonstrating the generalization advantage of the proposed transfer method. To verify the robustness of the proposed method，this study applies VL-Bolt to three publicly available datasets and compares its classification performance with that of end-to-end fine-tuning model. On the fine-grained classification datasets Flowers102 and StanfordCars，VL-Bolt achieves substantial improvements，particularly on StanfordCars，where it surpasses the end-to-end fine-tuning model by 10.7%. On the Caltech101 dataset，which is characterized by large interclass variations，VL-Bolt still demonstrates strong task adaptability，further validating the robustness of the proposed approach. Ablation studies confirm the contribution of each component of our method and its bolt classification task compatibility. The TA-VFA architecture avoids the intraclass semantic overlap issue，and its integration leads to a notable improvement in accuracy. The text feature decoupling mechanism can effectively mitigate interclass confusion to a certain extent. Notably，the progressive image encoder fine-tuning strategy guides the model to focus on target semantics，substantially improving accuracy. To identify the optimal layer depth for progressive fine-tuning，we analyze training loss and validation accuracy curves and find that unfreezing only Transformer layers 5 to 11 achieves optimal performance. Compared with end-to-end fine-tuning，the progressive image encoder fine-tuning strategy effectively stabilizes the training process and mitigates overfitting. In visualization experiments，the near-orthogonality of interclass text feature similarities validates the effectiveness of the proposed text feature decoupling loss. Moreover，using t-distributed stochastic neighbor embedding，we visualize the feature spaces learned by image-text contrastive learning，CLIP ViT-B/32，and VL-Bolt. Compared with the other methods，VL-Bolt shows better interclass separability with more natural boundary distributions，achieving reasonable balance in feature representation. Gradient-weighted class activation map visualizations further reveal that VL-Bolt focuses on bolt targets and defect regions while reducing attention to irrelevant background areas，thereby providing a solid foundation for downstream classification tasks.

The proposed VL-Bolt substantially improves the classification performance of VLMs in bolt defect classification tasks. Specifically，the TFD loss and TA-VFA architecture demonstrate strong task adaptability and effectively mitigate the challenge of intraclass semantic overlap while reducing the burden of data preparation. Moreover，the progressive fine-tuning strategy for the image encoder suppresses overfitting and directs the model’s attention toward defect-relevant regions. Overall，this work provides new insights into the practical application of VLMs in power transmission line inspection.