A sewer network multi-label classification method
By combining convolutional neural networks, graph convolutional neural networks, and Transformer decoders, the problem of low accuracy in multi-label classification of drainage pipe networks in existing technologies is solved, and more accurate multi-label defect classification of sewer images is achieved.
CN122336375APending Publication Date: 2026-07-03成都兴蓉市政设施管理有限公司 +1
View PDF 0 Cites 0 Cited by
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 成都兴蓉市政设施管理有限公司
- Filing Date
- 2026-03-22
- Publication Date
- 2026-07-03
Smart Images
Figure CN122336375A_ABST
Abstract
This application provides a multi-label classification method for drainage pipe networks. It employs a convolutional neural network to extract multi-level image features from sewer images; and a graph convolutional neural network to extract defect label information from the sewer images. The label information is then used as a query, and the multi-level image features are fused as keys and values into a Transformer decoder. Finally, the cross-interest module in the Transformer decoder is used to query whether relevant features exist in the sewer images, resulting in a multi-label defect classification of the sewer images. This method dynamically aligns image content with defect semantics through cross-modal interaction, thereby achieving a more accurate multi-label defect classification of sewer images.
Need to check novelty before this filing date? Find Prior Art