This paper proposes a lightweight neural network method based on UNet to accurately detect and localize high-density, low signal-to-noise ratio (SNR) sub-diffraction fluorescence spots in high-throughput fluorescence microscopy imaging. This method combines a squeeze and excitation channel-wise attention mechanism with a residual module to optimize feature information. A density map and offset multioutput architecture are also constructed for direct detection and subpixel localization. The proposed method has been verified on public and simulated datasets, and outperforms current algorithms for low SNR and high-density fluorescent spot detection. Notably, the detection performance of the proposed method is excellent for high-density fluorescent spot that reaches the diffraction limit, such as in images with a resolution of 128 × 128 pixels having 1200 fluorescent spots. The spot detection accuracy (F1 score) of the proposed algorithm exceeds 97.6%, and the localization error is 0.115 pixel. Compared with the latest deepBlink method, the F1 of the proposed algorithm has improved by 16.2 percentage points, and the localization error has been reduced by 0.63 pixel.