Interventional therapy for coronary bifurcation lesions has always been an intractable problem in percutaneous coronary intervention (PCI). Dual-guidewire detection can greatly assist physicians in interventional therapy of bifurcated lesions. Nevertheless, this task often comes with the challenges of X-ray images with a low signal noise ratio (SNR) as well as the thinner structure of the guidewire compared to other interventional tools. In this paper, a fully automatic detection method based on an improved U-Net and the modified focal loss is proposed for dual-guidewire segmentation in 2D X-ray fluoroscopy, which accomplishes accurate and robust segmentation. The main contributions of this paper are twofold: (1) the proposed method not only addresses the extreme foreground-background class imbalance generated by the slender guidewire structure but also solves the problem of misclassified examples caused by the guidewire-like structures and contrast agents; (2) the running speed is about 8 frames per second, which reaches near-real-time processing speed. Furthermore, data augmentation algorithms and transfer learning are used to further improve performance. The proposed method was verified on clinical 2D X-ray image sequences of 30 patients, in which the F1-score reached 0.932. The experiment results indicated that our approach is promising for assisting bifurcation lesion surgery.