Fourier single-pixel imaging (FSI) uses a digital projector to illuminate the target with Fourier basis patterns, and captures the back-scattered light with a photodetector to reconstruct a high-quality target image. Like other single-pixel imaging (SPI) schemes, FSI requires the projector to be focused on the target for best performance. In case the projector lens is defocused, the projected patterns are blurred and their interaction with the target produces a low-quality image. To address this problem, we propose a fast, adaptive, and highly-scalable deep learning (DL) approach for projector defocus compensation in FSI. Specifically, we employ a deep convolutional neural network (DCNN), which learns to offset the effects of projector defocusing through training on a large image set reconstructed with varying defocus parameters. The model is further trained on experimental data to make it robust against system bias. Experimental results demonstrate the efficacy of our method in reconstructing high-quality images at high projector defocusing. Comparative results indicate the superiority of our method over conventional FSI and existing projector defocus rectification method. The proposed work can also be extended to other SPI methods influenced by projector defocusing, and open avenues for applying DL to correct optical anomalies in SPI.