The laser directed energy deposition technology can be used for additive/subtractive hybrid manufacturing (ASHM). ASHM can realize the manufacturing of some complex parts, such as curved parts. Curved parts will inevitably have some defects during the manufacturing process, which affects the sustainable development of the parts. However, it is difficult to detect these defects, due to the edge blur of the surface and the difficulty of defect feature extraction. To solve the above problems, this paper proposes a surface quality detection method of curved parts based on blurry inpainting network. Firstly, the error effect of the curved surface on the surface quality detection is quantitatively analyzed. An ECANet‐DPDNet blurry inpainting network model is proposed to effectively reduce the adverse effect of edge blurring on surface quality detection. Then, six feature parameters of the repaired curved surface image are extracted. The BP neural network trained by the feature parameters is used to predict the curved surface roughness. An adaptive feature enhancement algorithm is also established to highlight the feature information of the defect regions. On this basis, two kinds of surface defects are identified by using our proposed method based on adaptive threshold segmentation matrix and interference region filtering. Finally, the constructed Support Vector Machine (SVM) defect type recognition model was trained using the 15 feature parameters extracted from the defect region. The experimental results show that the accuracy rates for the judgment of scratch defects and pit defects can reach 96.00% and 94.00%, respectively. Therefore, the research on the surface quality detection of metal curved parts contributes to the intelligent and sustainable development of ASHM technology.This article is protected by copyright. All rights reserved.