In recent years, frequency dynamic convolution (FDConv) has shown strong performance in sound event detection (SED). However, it primarily focuses on frequency modeling, neglecting dynamic temporal variations and time-frequency interactions, which limits its ability to capture key patterns in non-stationary events. To address this, we propose a time-frequency dual-stream attention (TFDSA) mechanism that integrates temporal attention pooling (TAP) and spectral feature booster (SFB) into conventional attention, enabling separate modeling of non-stationary and stationary structures. TFDSA employs a tri-branch design, including differential perception, gated modulation, and statistical smoothing, to enhance features and provide saliency guidance along both temporal and spectral dimensions. Experiments on the DESED dataset demonstrate that TFDSA outperforms the FDConv baseline, improving PSDS1 and PSDS2 by 2.53% and 2.28%, respectively, and surpasses multiple mainstream attention mechanisms. Visual analysis further confirms its effectiveness in capturing time-frequency saliency.