A Semantic Transferable Feature Extraction Strategy for Cross-Domain Detection

Surface defect detection is important for ensuring industrial manufacturing quality. Convolutional neural networks significantly improve the detection performance. However, surface defects exhibit significant differences in appearance and background due to complex working conditions. Traditional deep models show limited generalization for domain shift caused by data differences. To address the domain shift from the defect appearance change, a task-oriented domain confusion network (TDCN) is developed, which attempts to explicitly link its parameter structure with the structural feature of the target data, guiding the network to form a transferable causal mapping system, so that the solved feature can uniquely represent the intrinsic information of the target data. Specifically, defect data generation and inverse solution methods are studied to establish a feature-recoding strategy, which is used as prior knowledge to drive an adversarial function, transforming causal estimation into a Nash equilibrium of the network to eliminate noise bias caused by hard-to-transfer features. To respond to the model deviation under Nash equilibrium, a feature semantic storage dictionary is established to align the local and global semantic features. Ultimately, the parameter gradient evolution logic guided by classification is studied, which adaptively fits an unbiased causal estimation model, improving the transfer stability and cross-domain detection accuracy of defect features between multiple environments. Extensive experiments have proved that our TDCN outperforms the most state-of-the-art methods, especially for detection tasks with scarce data.