In industrial precision manufacturing， the small field depths of the imaging systems of visual inspection equipment can make them susceptible to defocus blurring. This significantly degrades their detection effect. To address this issue， this paper proposes a uniform defocus blind deblurring network （UDBD-Net）. First， a uniform defocus blur kernel estimation net for extracting the characteristics of out-of-focus blurring and accurately estimating the blur kernel is proposed. Second， a non-blind deconvolution network， which is used for learning and estimating the unknown quantity in the feature-based Wiener deconvolution （FWD） formula so as to accurately generate the latent features of blurred images， is presented. Finally， the use of an encoder–decoder net to enhance the details of the recovered image and remove the artifacts is detailed. The experimental results indicate peak signal-to-noise ratio （PSNR） values of 31.16 dB and 36.16 dB for UDBD-Net on the images of DIV2K and GOPRO test sets， respectively. Compared with extant blind deblurring methods， the proposed method can restore deblurred images with higher quality and more naturalness without significantly increasing the model inference time. Furthermore， UDBD-Net can achieve a good deblurring effect on real uniformly defocused blurred images and can considerably improve the detection effect of industrial vision detection algorithms on such images.