An extratropical cyclone is an important weather system in mid-latitude regions and has a profound impact on global weather and climate. The frontal system is a key component of the cyclone, and its accurate identification is crucial for improving the accuracy of weather forecasts. In recent years, deep learning methods have been applied to automatic frontal recognition, but research on frontal identification over the Eurasian continent has been limited, with a focus on detecting single-type fronts. Achieving simultaneous recognition and classification of cold and warm fronts remains a major challenge. Therefore, this paper proposes a method for the synchronous recognition of cold and warm fronts based on the Detection Transformer (DETR) model called DETR-FRONT (DETR-FRO). This method constructs RGB feature images using meteorological variables, such as sea level pressure, temperature advection, temperature, and relative humidity at 850 hPa. The DETR model is trained using an artificially created cold and warm front label set, enabling automatic synchronous recognition and classification of both types of fronts. DETR-FRO improves the original DETR model by enhancing its transmission channel and incorporating multiple meteorological variables to construct RGB feature images. This modification strengthens the ability of the model to capture and express frontal features, leading to improved recognition performance for cold and warm fronts. Multidimensional validation results show that DETR-FRO can simultaneously identify cold and warm fronts in extratropical cyclones over the Eurasian continent with high accuracy and stability. In a typical snowstorm case study, the DETR-FRO model accurately reconstructs the cold and warm fronts and their evolution processes. The recognition results are consistent with dynamic and thermodynamic fields, further confirming the model’s generalization ability and meteorological interpretability. This improvement provides new technical support for weather forecasting and the study of extratropical cyclones. In addition, the long-term frontal dataset created using DETR-FRO, covering the period from 1995 to 2024, reveals the seasonal distribution characteristics of cold and warm front frequencies in extratropical cyclones over the Eurasian continent at the climate scale. This information contributes to a deeper understanding of the climatic characteristics of cold and warm fronts in extratropical cyclones.