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The accurate and fast localization of key components on high-speed railways in operation serves is one of the foundations for fault diagnosis of high-speed railways. However, utilizing ordinary industrial cameras for data collection on operational high-speed railways often results in motion blur or misses key components. On the other hand, training object detection models with data collected by ultra-high-speed spike cameras encounters challenges, such as difficulties in data annotation and model training. To this end, this paper proposes a detection method for key components on high-speed railways that leverages reconstruction based on spike data. Firstly, based on the principle of spike triggering, the light intensity was restored at different points following the inter-spike interval. Then, local adjustments to light intensity were made through global light intensity calculations. The adjusted results were further filtered and locally enhanced to reconstruct high-resolution grayscale images. Subsequently, after data cleaning and annotation based on the reconstructed images, the annotated data were used to train the detection model for key components on high-speed railways. Finally, the trained model was accelerated using TensorRT. Experimental results showed that the accelerated model achieved an accuracy of 98.7% and a single-frame inference rate of 3.5 ms both on average. The study findings lay a foundation for the engineering applications of the proposed algorithm .