SDH-FCOS:An Efficient Neural Network for Defect Detection in Urban Underground Pipelines  

作　　者：Bin Zhou Bo Li Wenfei Lan Congwen Tian Wei Yao 

机构地区：[1]College of Computer Science,South-Central Minzu University,Wuhan,430074,China

出　　处：《Computers, Materials & Continua》2024年第1期633-652,共20页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China under Grant No.61976226;the Research and Academic Team of South-CentralMinzu University under Grant No.KTZ20050.

摘　　要：Urban underground pipelines are an important infrastructure in cities,and timely investigation of problems in underground pipelines can help ensure the normal operation of cities.Owing to the growing demand for defect detection in urban underground pipelines,this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector(FCOS),called spatial pyramid pooling-fast(SPPF)feature fusion and dual detection heads based on FCOS(SDH-FCOS)model.This study improved the feature fusion component of the model network based on FCOS,introduced an SPPF network structure behind the last output feature layer of the backbone network,fused the local and global features,added a top-down path to accelerate the circulation of shallowinformation,and enriched the semantic information acquired by shallow features.The ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection heads.The experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately;the average accuracy was improved by 2.7% compared with the original FCOS model,reducing the leakage rate to a large extent and achieving real-time detection.Also,our model achieved a good trade-off between accuracy and speed compared with other mainstream methods.This proved the effectiveness of the proposed model.

关 键 词：Urban underground pipelines defect detection SDH-FCOS feature fusion SPPF dual detection heads 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]