Find the Assembly Mistakes: Error Segmentation for Industrial Applications
This work addresses the need for error localization in assembly and maintenance procedures to improve worker efficiency and reduce downtime in industrial applications, representing a novel application of change detection.
The paper tackles the problem of localizing assembly errors in industrial settings by proposing StateDiffNet, which detects differences between correct and test images, achieving the first successful localization on real ego-centric video data for unseen states and error types.
Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although assembly state recognition is gaining attention, none of the current works investigate assembly error localization. Therefore, we propose StateDiffNet, which localizes assembly errors based on detecting the differences between a (correct) intended assembly state and a test image from a similar viewpoint. StateDiffNet is trained on synthetically generated image pairs, providing full control over the type of meaningful change that should be detected. The proposed approach is the first to correctly localize assembly errors taken from real ego-centric video data for both states and error types that are never presented during training. Furthermore, the deployment of change detection to this industrial application provides valuable insights and considerations into the mechanisms of state-of-the-art change detection algorithms. The code and data generation pipeline are publicly available at: https://timschoonbeek.github.io/error_seg.