YINHong-yi,SUNQuan,XIONGYou-liang,et al.A Method for Identifying Defects in Drainage Networks Based on Transformer and CNN[J].China Water & Wastewater,2025,41(5):123-129.
A Method for Identifying Defects in Drainage Networks Based on Transformer and CNN
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第41卷
Number:
第5期
Page:
123-129
Column:
Date of publication:
2025-03-01
- Keywords:
- drainage network inspection; defect identification; RT-DETR; attention mechanism; inner-MPDIoU
- Abstract:
- Given the significant workload and time-consuming nature associated with traditional manual detection and identification of drainage network defects, this paper proposed an automated identification method for drainage network defects utilizing convolutional neural network (CNN) in conjunction with Transformer. The proposed model is designated as RFCBAM-CGA-RTDETR. Based on RT-DETR, the model initially employed the receptive field convolutional block attention module to enhance the feature extraction capability of the network within the backbone. Subsequently, it utilized the cascaded grouped attention module to mitigate computational complexity during the encoding process while preserving the capacity to extract deep features. Finally, the inner-MPDIoU algorithm was applied to optimize the convergence of the predicted bounding box regression. The model was utilized to detect 14 types of drainage network defects. Compared with other target detection models, this model could accurately identify various drainage network defects. Specifically, the average precision (mAP50) was 8.6% and 12% higher than that of RT-DETR and YOLOv8, respectively. All evaluation metrics were superior to those of other models. These findings confirm the effectiveness and generalizability of the model in identifying drainage network defects.
Last Update:
2025-03-01