Dust deposition can affect the normal operation of equipment. To accurately and efficiently detect dust on equipment and formulate a scientific cleaning strategy, a lightweight dust deposition detection method based on Fast-UNet was proposed. By effectively pruning UNet and adopting max pooling and bilinear interpolation for down-sampling and up-sampling operations, the parameter redundancy was reduced, and a compact basic network was obtained. The lightweight Ghost Module was used to replace the ordinary convolution in the basic network, further reducing the complexity of the network. An convolutional block attention module(CBAM) that integrated channel and spatial attention was embedded in the encoding process, which made the network pay more attention to the target area while introducing minimal parameters. Experiments on a simulated dust deposition dataset show that, compared with the original model, Fast-UNet reduces the number of parameters by 99.6%, decreases computational complexity by 98.7%, achieves an inference speed of 94.18 frames per second, and maintains a recognition accuracy of 91.17%. Compared with five other mainstream segmentation models, Fast-UNet also demonstrates advantages in both accuracy and speed. This method meets the needs of dust detection for both accuracy and efficiency, providing a technical reference for dust quantitative analysis.