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城市排水管道缺陷智能檢測方法研究
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1.西安建筑科技大學(xué)建筑設備科學(xué)與工程學(xué)院;2.西安建筑科技大學(xué)管理學(xué)院

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陜西省自然科學(xué)基金面上項目(2024JC-YBMS-286)


Research on intelligent detection method of defects in urban drainage pipelines
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    摘要:

    針對城市排水管道缺陷自動(dòng)化檢測準確率低、目標定位不準確的問(wèn)題,提出一種改進(jìn)YOLOv8的排水管道缺陷檢測模型。該模型在基線(xiàn)模型中引入感受野注意力卷積,并構建了C2F_RFAConv模塊,通過(guò)空間感受野與卷積交互自適應學(xué)習的方式,增強模型對缺陷特征的提取能力;同時(shí),提出一種混合注意力高低階特征融合網(wǎng)絡(luò ),將Backbone和Neck輸出的三種不同尺度的低階特征和高階特征進(jìn)行有效融合,增強模型學(xué)習圖像全局上下文信息的能力;此外,對影響邊界框回歸的重疊情況、中心點(diǎn)距離、寬高偏差等因素進(jìn)行了綜合分析,設計Inner-MPDIoU損失函數,使模型適應不同尺寸的缺陷檢測任務(wù),提高缺陷目標邊界框的定位準確率。經(jīng)過(guò)實(shí)驗驗證發(fā)現,改進(jìn)后的模型取得了93.9%的平均檢測準確率,較改進(jìn)之前提升3.7%,漏檢率和誤檢率僅為9.1%和17.6%,較改進(jìn)之前分別降低3.2%和2.7%。

    Abstract:

    To address the problems of low automatic detection accuracy and inaccurate target positioning of urban drainage pipeline defects, an improved YOLOv8 drainage pipeline defect detection model is proposed. This model introduces receptive field attention convolution into the baseline model and constructs the C2F_RFAConv module to enhance the model's ability to extract defect features through interactive adaptive learning of spatial receptive fields and convolutions. Additionally, a hybrid attention high-order and-low-order feature fusion network is proposed, which effectively fuses the low-order and high-order features of three different scales output by the backbone and neck, enhancing the model's ability to learn the global contextual information of the image. The Inner-MPDIoU loss function is designed by comprehensively analyzing factors affecting bounding box regression, such as overlap, center point distance, and width-height deviation. This function enables the model to adapt to defect detection tasks of different sizes and improves the positioning accuracy of the defect target boundary box. Experimental validation shows that the improved model achieves an average detection accuracy of 93.9%, which is a 3.7% increase compared to the baseline model; the missed detection rate and false detection rate are reduced to 9.1% and 17.6%, representing decreases of 3.2% and 2.7%, respectively, compared to the baseline model.

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雷江浩,劉光宇,張溫,程靜.城市排水管道缺陷智能檢測方法研究計算機測量與控制[J].,2024,32(12):81-87.

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  • 收稿日期:2024-05-30
  • 最后修改日期:2024-07-01
  • 錄用日期:2024-07-01
  • 在線(xiàn)發(fā)布日期: 2024-12-24
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