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A new method combined wit multi-view matching and feature extraction technique is developed to detect manhole covers on the\udstreets using close-range images combined with GPS/IMU and LINDAR data. The covers are an important target on the road traffic as\udsame as transport signs, traffic lights and zebra crossing but with more unified shapes. However, the different shoot angle and\uddistance, ground material, complex street scene especially its shadow, and cars in the road have a great impact on the cover detection\udrate. The paper introduces a new method in edge detection and feature extraction in order to overcome these difficulties and greatly\udimprove the detection rate. The LIDAR data are used to do scene segmentation and the street scene and cars are excluded from the \udroads. And edge detection method base on canny which sensitive to arcs and ellipses is applied on the segmented road scene and the\udinteresting areas contain arcs are extracted and fitted to ellipse. The ellipse are then resampled for invariance to shooting angle and\uddistance and then are matched to adjacent images for further checking if covers and . More than 1000 images with different scenes \udare used in our tests and the detection rate is analyzed. The results verified our method have its advantages in correct covers\uddetection in the complex street scene

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

MANHOLE COVER DETECTION USING VEHICLE-BASED MULTI-SENSOR DATA