<p>Currently, most trains are equipped with dedicated cameras for capturing pantograph videos. Pantographs are core to the high-speed-railway pantograph-catenary system, and their failure directly affects the normal operation of high-speed trains. However, given the complex and variable real-world operational conditions of high-speed railways, there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video. Hence, it is of paramount importance to maintain real-time monitoring and analysis of pantographs. Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs, utilizing a fusion of self-attention and convolution features. We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies. Compared with traditional methods, this approach achieves high recall and accuracy in pantograph recognition, accurately pinpointing issues like discharge sparks, pantograph horns, and carbon pantograph-slide malfunctions. After experimentation and validation with actual surveillance videos of electric multiple-unit train, our algorithmic model demonstrates real-time, high-accuracy performance even under complex operational conditions.</p>