To address the problems of traditional machine vision algorithms such as low detection accuracy， inability to adapt to environmental changes and noise influence in surface defect detection， a improved UNet++ based on Wavelet Transform （WT） — WT-U-Net++ was proposed. Firstly， the high frequency and low frequency components of the defect image were obtained by the WT， and the detailed features of the high and low frequency components were extracted by the multi-scale module MCI （Mix-Conv Inception）. Secondly， the detailed features extracted by MCI module were fused with the original image， and the fusion results were used as the input of the improved UNet++. Thirdly， in the downsampling stage of UNet++， channel attention module was introduced to enable the network to capture more contextual semantic information and improve the quality of cross-layer feature cascade at the same time. In the upsampling stage， deconvolution was adopted to recover more defect details. Finally， the best result was selected from the multiple output of UNet++ as the detection result. Experimental results on three public defect datasets of rail， magnetic tile and silicon steel oil stain show that compared with the sub-optimal algorithm UNet++， WT-U-Net ++ has the Intersection over Union （IoU） increased by 7.98%， 4.63%， and 8.74% respectively， and the Dice Similarity Coefficient （DSC） improved by 4.26%， 2.99% and 4.64% respectively.