A New Foreground-Perception Cycle-Consistent Adversarial Network for Surface Defect Detection With Limited High-Noise Samples

Surface defect detection (SDD) is critical in the smart manufacturing systems to ensure product quality. Nevertheless, the defective samples are always insufficient, and there exists high-noise backgrounds in the samples from the real-world SDD applications, which could significantly affect SDD models. To remedy the drawbacks, a new foreground-perception cycle-consistent adversarial network (FCGAN) is proposed, and it can synthesize high-quality pseudodefect images by recognizing the foreground of samples. First, the attention networks are combined with the generator and discriminator to guide the training process. Second, the discriminative foreground is recognized and used to synthesize coordinated and realistic pseudodefect images. Third, the adversarial training for FCGAN is designed. The experiments are carried out on two well-known datasets and a real-world SDD for glass bottles. The results show that FCGAN can produce better pseudodefect images than the state-of-the-art generative adversarial networks, and it can promote the detection accuracy of SDD as well.