數(shù)字圖像處理在橋梁結(jié)構(gòu)變形檢測(cè)的應(yīng)用研究
信息技術(shù)與網(wǎng)絡(luò)安全
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)
摘要： 針對(duì)大型橋梁在施工階段和運(yùn)營期間發(fā)生結(jié)構(gòu)變形問題，目前缺乏自動(dòng)化、高頻、實(shí)時(shí)與長期并且精確的檢測(cè)手段。在數(shù)字圖像處理與深度學(xué)習(xí)理論基礎(chǔ)下，提出一種適用于大型橋梁結(jié)構(gòu)變形的非接觸式檢測(cè)方法，并以此方法研發(fā)系統(tǒng)，可以對(duì)橋梁多個(gè)目標(biāo)結(jié)構(gòu)進(jìn)行同步動(dòng)態(tài)監(jiān)測(cè)。該方法首先通過高分辨率攝影設(shè)備獲取橋梁結(jié)構(gòu)的動(dòng)態(tài)視頻序列圖像；其次對(duì)圖像進(jìn)行預(yù)處理去除天氣等外部因素對(duì)圖像的影響；然后提取圖像ROI確定待處理的具體橋梁結(jié)構(gòu)部位；最后對(duì)深度學(xué)習(xí)中YOLOv3算法進(jìn)行改進(jìn)并結(jié)合改進(jìn)后的SURF算法實(shí)現(xiàn)橋梁結(jié)構(gòu)的變形檢測(cè)。實(shí)驗(yàn)結(jié)果表明，算法檢測(cè)速度在20～30 f/s之間，目標(biāo)距離100 m時(shí)，算法檢測(cè)精度在0.3 mm以內(nèi)，檢測(cè)精度高，可有效反映橋梁結(jié)構(gòu)變形情況。
中圖分類號(hào)： TP391.41
文獻(xiàn)標(biāo)識(shí)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(cè)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(shí)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(cè)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。基于各種因素，橋梁在施工與運(yùn)營期間會(huì)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(shí)地檢測(cè)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)。
本文詳細(xì)內(nèi)容請(qǐng)下載:http://m.jysgc.com/resource/share/2000003375
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)
此內(nèi)容為AET網(wǎng)站原創(chuàng)，未經(jīng)授權(quán)禁止轉(zhuǎn)載。