Railway fastener inspection faces challenges in both detection speed and accuracy due to inconspicuous defect features and complex background contrast. To achieve faster and more precise fastener identification, an improved YOLOv10-based detection algorithm was proposed. Specifically, an attention mechanism (SPPF-Attention) was introduced into the SPPF module to enhance feature recalibration and improve focus on critical regions. Concurrently, a dual attention block (C2f-DAB) was embedded in the C2f module, which combines channel-space attention with parallel attention to strengthen multi-dimensional feature extraction. Experimental results on a self-built railway fastener dataset demonstrates that the original YOLOv10 achieves an average precision of 88.1%. The SPPF-Attention variant achieves an improvement to 89.3%, while the C2f-DAB implementation gains 2.1% over the baseline. All three models maintained real-time performance (42, 41, and 39 frames/s, respectively), meeting practical inspection requirements. The effectiveness of attention mechanism and dual attention block network model in railway fastener detection task was verified through comparative experiments, providing theoretical basis and practical reference for research in related fields.