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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/64780

    Title: 影像辨識技術應用於鋪面破壞調查之研究;A Study of Image Recognition Technology Applying to Pavement Distress
    Authors: 陳林君;Chen,Lin-jyun
    Contributors: 土木工程學系
    Keywords: 鋪面狀況指標;道路巡查;影像形態校正;直線與車道偵測;pavement condition index;road inspection;image morphological adjustment;straight line and lane detection
    Date: 2014-07-21
    Issue Date: 2014-10-15 14:23:58 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 巡查為道路養護的基本工作,鋪面破壞易造成用路人行車安全;而鋪面破壞也間接的影響鋪面平整度,而國內各級道路主管機關也積極推動道路齊平,為了有效落實巡查作業,近年來國內外均有研發道路檢測設備;將檢測設備搭載於車輛執行巡查作業,而設備中影像擷取器使用最新科技產品,如智慧型手機、平板電腦、CCD Camera等影像擷取設備,以影像方式蒐集鋪面影像,故本研究為以影像為基礎,研擬自動化巡查與辨識系統,將巡查蒐集的鋪面影像,利用影像處理技術將鋪面破壞特徵擷取,並將破壞影像進行量測,以鋪面狀況指標將破壞類型之嚴重程度作判定。
    本研究為利用CCD Camera進行鋪面影像拍攝,將蒐集之鋪面影像進行破壞特徵辨識。相機架設於車輛中所拍攝為傾斜影像,使用形態校正轉為正投影。界定鋪面破壞特徵辨識範圍,使用直線與車道偵測方法偵測車道標線;以標線內作為辨識範圍界定,裁切辨識範圍以外的影像背景。接著以影像處理技術將影像進行光源校正、影像二值化及形態學中的侵蝕與膨脹、邊框刪除、填滿等方法進行破壞特徵擷取。最後將破壞特徵使用影像量測方式將鋪面破壞結果計算出。
    ;Road inspection is the groundwork for road maintenance. Pavement distress tends to put the safety of the road-users are at risk and impact the pavement smoothness indirectly. In addition to the efforts made by domestic road authorities in actively implementing the plan of road smoothness, R&D of road testing equipment, which are built in vehicles, has been made at home and abroad for the past few years in order to facilitate road inspection. Image capture devices featuring the latest technologies, such as smart phones, tablets and CCD Cameras, are installed in the testing equipment to collect photographic images of the pavement. Therefore, in this study, images are used as the basis. An automatic inspection and recognition system is developed to capture pavement images. An image processing technology is then used to capture pavement distress feature, measure the distress images and judge the severity of damage with the pavement condition index.
    This study involves using CCD Cameras to take pictures of the pavement and recognizing the pavement distress feature out of them. The photographic images captured by the cameras in the vehicle are slant and then corrected orthographically. The straight line and lane detection is employed to detect the lane markings which are taken as the limits of the recognition scope of pavement distress feature. The image background outside of the scope is cropped. Then the pavement distress feature is captured through illumination adjustment made with image processing technology, image binary processing, and methodology used in morphology such as erosion and dilation, border removal and hole filling. Finally, the pavement distress feature is calculated with image measurements.
    This research result is then verified against the calibrated images. With distance and area measurements, it is determined that the distance for capturing complete pavement image for detection is 2.5 meters. The feature of potholes, manhole, longitudinal / transverse cracking is cropped out of the pavement images with image process technology. The area, length and width of the pavement distress is measured with image measuring technology. At last, the measurements are used to severity of the pavement distress, compared against the pavement condition index. This technology is incorporated into the road inspection procedure by importing the collected data for pavement distress into the Pavement Management System as the reference in assessing the pavement engineering life cycle and determining the maintenance cycles in the future.
    Appears in Collections:[土木工程研究所] 博碩士論文

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