Unsupervised Wood Surface Anomaly Detection via Enhanced GAN with Residual Dense and Attention Modules

Reliable surface-defect inspection is a prerequisite for modern woodprocessing lines, yet manually labelled defect images are inherently scarce and imbalanced. We present ERA-GANomaly, an unsupervised anomaly-detection framework that combines an encoder–decoder–re-encoder back-bone with Residual Dense Blocks RDBs and lightweight Efficient Channel Attention ECA to emphasise salient textures. Experiments on three wood-defect datasets show that ERA-GANomaly attains 92.4 accuracy and a macro-F1 of 83.0 , outperforming representative unsupervised baselines such as GANomaly, EGBAD and AnoGAN. Ablation studies verify that both ECA and RDB modules contribute markedly to detecting subtle de-fects—including cracks, chips and bark inclusions. These findings indicate that ERA-GANomaly offers a practical, label-free solution for industrial sur-face-defect screening.