数字图像处理在桥梁结构变形检测的应用研究
信息技术与网络安全
柳胜超，王夏黎，张 琪，赵嘉兴
(长安大学 信息工程学院，陕西 西安710064)
摘要： 针对大型桥梁在施工阶段和运营期间发生结构变形问题，目前缺乏自动化、高频、实时与长期并且精确的检测手段。在数字图像处理与深度学习理论基础下，提出一种适用于大型桥梁结构变形的非接触式检测方法，并以此方法研发系统，可以对桥梁多个目标结构进行同步动态监测。该方法首先通过高分辨率摄影设备获取桥梁结构的动态视频序列图像；其次对图像进行预处理去除天气等外部因素对图像的影响；然后提取图像ROI确定待处理的具体桥梁结构部位；最后对深度学习中YOLOv3算法进行改进并结合改进后的SURF算法实现桥梁结构的变形检测。实验结果表明，算法检测速度在20～30 f/s之间，目标距离100 m时，算法检测精度在0.3 mm以内，检测精度高，可有效反映桥梁结构变形情况。
中圖分類號： TP391.41
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.02.005
引用格式： 柳勝超，王夏黎，張琪，等. 數(shù)字圖像處理在橋梁結(jié)構(gòu)變形檢測的應(yīng)用研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(2)：24-32.
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.02.005
引用格式： 柳勝超，王夏黎，張琪，等. 數(shù)字圖像處理在橋梁結(jié)構(gòu)變形檢測的應(yīng)用研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(2)：24-32.
Application research of digital image processing in deformation detection of bridge structures
Liu Shengchao，Wang Xiali，Zhang Qi，Zhao Jiaxing
(School of Information Engineering，Chang′an University，Xi′an 710064，China)
Abstract： In view of the structural deformation of large bridges during construction and operation, there is currently a lack of automated, high-frequency, real-time, long-term and accurate detection methods. Based on the theory of digital image processing and deep learning, this paper proposes a non-contact detection method suitable for large-scale bridge structure deformation, and uses this method to develop a system that can simultaneously dynamically monitor multiple target structures of the bridge. This method firstly obtains dynamic video sequence images of the bridge structure through high-resolution photography equipment; secondly, it preprocesses the image to remove the influence of external factors such as weather on the image; then it extracts the image ROI to determine the specific bridge structure to be processed; finally, the YOLOv3 algorithm is improved and combined with the improved SURF algorithm to realize the deformation detection of the bridge structure. Experimental results show that the detection speed of the algorithm is between 20 fps and 30 fps, when the target distance is 100 m, the detection accuracy of the algorithm is within 0.3 mm, and the detection accuracy is high, which effectively reflects the deformation of the bridge structure.
Key words : software engineering；bridge structure deformation；digital image processing；remote detection；deep learning；SURF algorithm
0 引言
橋梁在陸路交通中屬于一種特殊的道路結(jié)構(gòu)，是日常生活的基礎(chǔ)設(shè)施之一。自古至今，橋梁作為交通樞紐中較為重要的一環(huán)，其安全性一直是人們關(guān)注的焦點(diǎn)。橋梁安全性主要分為建設(shè)安全性與使用安全性?；诟鞣N因素，橋梁在施工與運(yùn)營期間會出現(xiàn)結(jié)構(gòu)變形[1-2]，橋梁變形程度能夠直接反映出橋梁的健康狀況。隨著國民經(jīng)濟(jì)的日益增長和近現(xiàn)代交通技術(shù)的不斷發(fā)展，橋梁的體積越來越大，橋梁結(jié)構(gòu)越來越復(fù)雜，橋梁的應(yīng)用環(huán)境越來越多樣。因此在橋梁建設(shè)過程中，如何實(shí)時地檢測橋梁的變形程度，以確保橋梁工程的安全就成為橋梁建設(shè)的一項(xiàng)重要技術(shù)。
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作者信息:
柳勝超，王夏黎，張 琪，趙嘉興
(長安大學(xué) 信息工程學(xué)院，陜西 西安710064)
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