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 

作者机构：College of Computer ScienceSouth-Central Minzu UniversityWuhan430074China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第1期

页      面：633-652页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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 

主　　题：Urban underground pipelines defect detection SDH-FCOS feature fusion SPPF dual detection heads 

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 *** 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)*** 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 *** ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection *** 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 ***,our model achieved a good trade-off between accuracy and speed compared with other mainstream *** proved the effectiveness of the proposed model.