Bidirectional Feature Pyramid Siamese Anomaly Detection Network With Cellular Anomaly Generation for Container Marking

Container marking anomaly detection (CMAD) aims to identify markings that deviate from a standard reference image. Currently, the manual execution of CMAD is inefficient and susceptible to errors. Existing machine vision methods are limited to detecting text-based or specific types of markings, rendering type-agnostic CMAD unattainable. To address this limitation, we propose an innovative framework for CMAD, named bidirectional feature pyramid siamese anomaly detection network (BiSiNet). BiSiNet consists of the Siamese encoder (SE), the bidirectional feature pyramid decoder (BiD), and the triplet classifier prediction head (TCPH). SE extracts the difference feature pyramid between the target and reference images. BiD decodes the difference feature pyramid in both top-down and bottom-up paths, effectively solving the problem of global information loss during decoding in traditional single-path decoder. TCPH utilizes both local and global features to predict outcomes at both the pixel and patch levels, integrating these results to resolve inconsistencies in predictions. BiSiNet not only possesses strong resistance to disturbances such as reflections and shadows but also has accurate localization capabilities. Due to the limited diversity and the difficulty in obtaining anomalous samples, we designed an innovative cellular anomaly generation strategy (CAGS). CAGS employs cellular noise to generate marking masks, creating realistic pseudo-marking images. Through innovative design, it produces four types of samples, thereby enhancing sample diversity and improving training generalization. Extensive experiments using our established ContainerMAD dataset have demonstrated that BiSiNet and CAGS outperform other methods in both CMAD and localization. In addition, favorable outcomes have been achieved on the MvtecAD dataset.