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Precise segmentation of defects is a key component of industrial quality control. This paper presents a comprehensive overview of contemporary methods utilising convolutional neural networks that have demonstrated practical efficacy. Depending on the application, semantic, instance-based, panoptic and hybrid segmentation methods are used to reliably detect material defects. Finally, prospects for industrial use are discussed, including the optimisation of hybrid methods, real-time capability and integration into existing production processes to ensure efficient, robust and practical defect detection.