The catenary insulator is a critical component of the traction power supply system for high-speed railways. It not only provides electrical control insulation but also plays an essential role in supporting the catenary arm structure. Therefore, the operational safety of the insulator is directly related to the stability of the entire high-speed railway system. However, the detection of insulator defects is often subject to various interferences due to the complex and dynamic railway environment, resulting in low detection accuracy. Moreover, traditional detection methods generally only identify the presence of defects but fail to provide specific semantic descriptions of these defects. This limitation significantly hampers the efficiency of fault diagnosis and maintenance operations. To address these challenges, this paper proposes a defect description method for insulators based on a diffusion model. This method optimizes existing detection technologies in several ways, enabling the model to not only detect insulator defects more accurately but also generate detailed textual descriptions of these defects.
Firstly, we designed a large-kernel spatial selection feature extraction network. Compared to traditional feature extraction networks, this network captures the feature information of insulator defects through larger spatial convolution kernels, significantly enhancing the model's ability to extract insulator defect features. The model can accurately identify potential defects in the insulator, even in complex backgrounds. Secondly, we proposed a detection decoder with a fusion diffusion mechanism based on the diffusion model. This decoder generates noise boxes and uses inverse Bayesian diffusion to restore predictions of the insulator's true bounding box, significantly improving the model's resistance to background interference. This innovation allows the model to more effectively isolate background noise in complex environments, thereby improving the accuracy of defect detection. Finally, to address the limitations of traditional detection models in semantic description, we designed an encoder and decoder based on a cross-attention mechanism to achieve cross-modal mapping between images and text. By using the BLIP model driven by a text filtering mechanism, the model can generate corresponding textual descriptions of the defects based on the detection results. The functionality not only provides maintenance personnel with more intuitive references but also greatly enhances the efficiency of fault handling. Experimental results validate the effectiveness of our method. The proposed insulator defect detection model achieved the mAP_0.5 of 93.04% and the AR and F1-score of up to 83.22% and 82.91%. The BLEU achieved 83.51%, with CIDEr of 1.94, ROUGE-L of 81.59%, METEOR of 51.50%, and SPICE of 37.88%.
The experimental results lead to the following conclusions: (1) Utilizing a large-kernel spatial selection feature extraction network as the image encoder enhances the insulator defect detection network's ability to focus on key features, thereby improving the model's detection accuracy. (2) To address the issue of insulator defect detection being easily disturbed by complex background environments, a detection decoder with a fusion diffusion mechanism was designed. This decoder performs inverse Bayesian diffusion on the noise boxes generated by the decoder, restoring the prediction of the insulator's true bounding box. The model's ability to resist background interference reduces the loss of semantic information related to insulator defects, and enhances the accuracy of the predicted bounding boxes. (3) A cross-modal mapping module was designed to map the relationship between insulator image defect features and text features. The language modeling encoder outputs a textual description of the insulator defects, completing the detection task. Thus, the proposed model not only offers higher detection accuracy but also generates accurate and detailed semantic descriptions of the defects, meeting the actual needs for insulator defect detection and description.