CLMFNet: Cross-Level Multimodal Fusion Network for RGB-T Semantic Segmentation of Distribution Network Lines

Accurate semantic segmentation is crucial in distribution network line monitoring to ensure the system's reliability and security. Due to the complexity of the environment and the diversity of devices, unimodal images (such as RGB images) struggle to provide enough information for effective segmentation. To address these challenges, the complementary nature of RGB and thermal infrared (TIR) images is leveraged to significantly enhance segmentation performance. Therefore, an innovative cross-level multimodal fusion network (CLMFNet) is proposed to improve the accuracy and robustness of semantic segmentation by integrating RGB and TIR data. A dual-branch architecture is employed in CLMFNet to extract features from both RGB and TIR images, which are then effectively integrated through a multimodal fusion strategy. Additionally, a cross-layer guidance mechanism is introduced to facilitate the complementation and optimization of features across different levels. CLMFNet was validated on a custom RGB-T dataset, and experimental results showed that it outperformed state-of-the-art methods in key metrics such as mean accuracy (mAcc) and mean intersection over union (mIoU), demonstrating its effectiveness in performing semantic segmentation in complex power distribution scenarios.