Surface discharge of oil-paper insulation is an important cause of insulation degradation in the oil-immersed transformer, and the accurate segmentation of discharge carbon traces is of great value for the judgment of discharge degree and the analysis of insulation degradation trend. In order to meet the challenges of complex edges, large size differences and oil infiltration interference of dendritic discharge carbon traces, MCH-UNet (multi-head coordinate-attention hardswish UNet) was proposed to realize the accurate segmentation of carbon traces. To address the problems of concentrated pixel values and weak contrast caused by the interference of oil stain infiltration and insufficient supplementary light, ACE (automatic color enhancement) algorithm was adopted for image enhancement of carbon trace samples. was proposed MHAC (multi-head attention convolution) module was designed to replace the conventional convolution module, for enhancing the fusion effect of carbon trace features at different scales while reducing the model complexity. In order to alleviate the semantic gap between the encoder and the decoder, and to improve the activation strength of the carbon trace features and the segmentation completeness, the CA attention mechanism was used instead of the original jump connection. The hardswish activation function was used instead of the original ReLU function to improve the gradient propagation during model training, and improve the ability to resolve complex boundaries. The experimental results showed that MCH-UNet had the best segmentation effect compared with UNet, UNet ++, UneXt, MALUNet, Att-UNet, EGE-UNet and other models. Compared with the benchmark model UNet, MCH-UNet improved MIoU (mean intersection over union), PA(pixel accuracy), and PE(precision rate) by 1.83%, 1.05%, and 4.33%, respectively. At the same time, the model parametric quantities and GFLOPs of MCH-UNet are 0.121 × 10⁶ and 0.074, respectively, which has a better lightweight advantage. The results could provide technical support for the accurate segmentation of discharge carbon trace and the assessment of oil-paper insulation health condition in the oil-immersed transformer.