Aiming at the problems of existing target detection algorithms, a real-time target detection and localization method based on fused differential convolution is proposed. Firstly, a backbone network with fused differential convolution is constructed to enhance feature extraction capabilities. Then, a feature fusion module and detection head with shared weights are designed to improve detection speed and accuracy. Finally, a multi-stage training strategy is formulated to further enhance accuracy. Experimental results on the pantograph detection dataset show that the proposed method achieves a frame detection speed of up to 149 frame/s on CPU hardware resources, with an whole mean average precision (mAP) of 81.20%. This is an improvement of 57 frame/s and 6 percentage points compared to the FemtoDet algorithm. Proposed method meets technical requirements for real-time and accurate triggering positioning tasks in high-speed railway scenarios.