Abstract: Pavement sealed cracks have significant impact on service life of pavement． A new method for pavement sealed crack detection based on improved faster R-CNN was proposed，aiming at solving the current lack of sealed crack detection technology． Firstly，the marked sample data set of pavement sealed crack was constructed based on the augmented sealed crack image set． Then they were divided into training set，verification set and test set accor- ding to the ratio of 6∶2∶2． Next，faster R-CNN model was employed in sealed cracks detection． Given that the faster R-CNN model has the demerits of miss detection and inaccurate positioning of sealed cracks，it was combined the feature extraction layers of VGG16，ZFNet and ＲesNet 50 networks． The results show that the detection accuracy of the VGG16 and faster R-CNN combination models can reach 0. 9031，which is the highest． Then，further im- provement was made by increasing the aspect ratio of the anchor of the sealed crack． The improved detection accu- racy reaches 0. 9073 and the original miss detection target can also be detected． Finally，detection and positioning accuracy between improved faster R-CNN and YOLOv2 model was compared． The result shows that improved faster R-CNN model can significantly enhance both detection and positioning accuracy．

Key words: pavement disease, sealed crack, detection method, feature extraction, multiple-scale localization, faster R-CNN, YOLOv2