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A composite transformer-Based Multi-Stage Defect Detection Architecture for Sewer Pipes

Computers, Materials & Continua 2024年第1期78卷 435-451页

作者： Zifeng Yu Xianfeng Li Lianpeng Sun Jinjun Zhu Jianxin Lin School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Environmental Science and Engineering Sun Yat-sen UniversityGuangzhou510275China Guangdong AIKE Environmental Science and Technology Co. Ltd.Zhongshan528400China

Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential *** recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential ***,these models are insufficient in terms of dataset complexity,model versatility and *** work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin *** based on this architecture is trained using a more comprehensive dataset containingmore classes of *** ablation studies on the modules of combined backbone Swin transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different *** model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of *** represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO *** addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.

关键词： Sewer pipe defect detection deep learning model optimization composite transformer

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