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基于YOLOv9-DS的预制混凝土叠合板质量检测模型研究

周勇 杨辉 周满旭 李琳

河北工业科技2026,Vol.43Issue(3):195-204,10.
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河北工业科技2026,Vol.43Issue(3):195-204,10.DOI:10.7535/hbgykj.2026yx03001

基于YOLOv9-DS的预制混凝土叠合板质量检测模型研究

Research on a quality inspection model for precast concrete composite slabs base on YOLOv9-DS

周勇 1杨辉 1周满旭 2李琳3

作者信息

  • 1. 中铁四局集团有限公司,安徽 合肥 230022
  • 2. 新疆农业大学水利与土木工程学院,新疆乌鲁木齐 830052||合肥工业大学土木与水利工程学院,安徽 合肥 230009
  • 3. 合肥工业大学土木与水利工程学院,安徽 合肥 230009
  • 折叠

摘要

Abstract

To address issues such as missing embedded parts and positional deviations during the production and installation of prefabricated concrete composite slabs,an improved YOLOv9-DS model for quality inspection of prefabricated concrete composite slabs was proposed.Data augmentation was further employed to expand the samples for model training and evaluation.First,using the YOLOv9 detection algorithm as the baseline framework,dynamic snake convolution(DSConv)and spatial and channel reconstruction convolution(SCConv)modules were incorporated.Second,Taking a prefabricated concrete factory as the engineering background,a detection dataset comprising three types of embedded parts,namely junction boxes,water pipe openings,and truss steel bars,was established.The results indicate that the improved YOLOv9-DS model achieves an overall mAP50 of 87.8%on the test set,with an mAP50 value of 89.9%for truss steel bars,89.0%for junction boxes,and 84.5%for water pipe openings,representing improvements of 3.2 percentage points,1.2 percentage points,and 3.3 percentage points over the original YOLOv9 model,respectively.The model contains 56.89M parameters,and the inference time was reduced from 13.4 ms to 9.7 ms,corresponding to a decrease of 27.6%.The proposed YOLOv9-DS model effectively improves both the accuracy and efficiency of multi-scale embedded parts recognition in composite slabs,demonstrating its capability to satisfy the requirements of robust and real-time detection in complex industrial scenarios.

关键词

土木建筑工程测量/混凝土叠合板/预埋件/YOLO/智能检测

Key words

civil and architectural engineering surveying/concrete composite slab/embedded parts/YOLO/intelligent detection

分类

建筑与水利

引用本文复制引用

周勇,杨辉,周满旭,李琳..基于YOLOv9-DS的预制混凝土叠合板质量检测模型研究[J].河北工业科技,2026,43(3):195-204,10.

基金项目

国家自然科学基金(52378152) (52378152)

新疆水利工程安全与水灾害防治重点实验室项目(ZDSYS-YJS-2023-19) (ZDSYS-YJS-2023-19)

河北工业科技

1008-1534

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