Paper Title
Crack Detection using Faster R-CNN and Point Feature Matching

Abstract
The detection of cracks on concrete surfaces is the most important step during the inspection of concrete structures. Inspection technology utilizing drones with image processing technique has recently been applied to crack assessment to overcome the drawbacks of visual inspection. However, identification of crack location requires watching the inspection videos to exactly locate cracks. This paper proposes a fast and easy method for cracks detection which provides the inspector with photos for the inspected structure that show the crack locations, and the inspector does not need to watch any inspection video. In the beginning, the drone will take a photo for the inspection element or region (Target Image). Then the drone will start searching for cracks using Faster Region- Convolution Natural network (Faster R-CNN) algorithm which allow for real time crack detection. When drone catches crack it will take a photo for this crack which will be called “reference image”. Point Feature matching algorithm is applied to locate the crack image “reference image” on the element image “target image”. This is achieved by matching the features points of the crack photo (reference image) with the feature’s points of the element photo (Target Image). Keywords - R-CN; Crack, Detection; Point Feature Matching; Image Processing