GAN-PD: generative adversarial networks for fabric defect generation towards precise detection.

Automated inspection of textile surfaces is challenged by the scarcity and geometric intricacy of real defect samples, especially the elongated, sub-millimetre scratches critical for quality grading. Existing generative approaches often prioritize image realism, thereby neglecting the geometric continuity and contextual fidelity essential for robust detection by modern systems. This paper introduces Generative Adversarial Networks for Fabric Defect Generation towards Precise Detection(GAN-PD), a task-oriented generative adversarial framework engineered to synthesize fabric anomalies with both high structural realism and detector relevance. GAN-PD's generator employs Local-Feature Residual modules to adaptively focus convolutional resources on defect-salient channels, while its discriminator utilizes columnar pooling to enforce directional continuity along defect axes. This synergistic design preserves high-frequency weave details, enforces millimetre-scale linearity, and mitigates fragmentation artifacts typical of isotropic convolutions. Experimental results on benchmark textile dataset show GAN-PD produces sharper linear anomalies, achieves more consistent texture fusion, and leads to significant improvements in detection accuracy for both one-stage and two-stage detectors compared to state-of-the-art GANs. These results validate the efficacy of shifting the paradigm from mere "defect generation" to "generation for defect detection." Ultimately, these findings demonstrate that reframing defect synthesis around downstream utility, rather than visual fidelity alone, can markedly enhance the robustness and accuracy of automated textile inspection systems.