| description abstract | Unmanned aerial vehicles (UAVs) have been widely used in the visual inspection of structural cracks. However, blurry images are inevitably generated during image collecting by UAVs, as they are caused by the motion of UAVs and other factors. This blur affects the retrieval of crack properties from images and degrades the accuracy and reliability of crack damage assessment. At present, blur detection and blurred image removal are mainly achieved manually, which is inefficient and fallible, especially for large image sets. To address this problem, a novel automatic blur detection method for UAV crack image data sets is proposed. This algorithm defines a blur detection metric named the edge average width difference (EAWD), which is based on the principle of a smaller difference between pixels of a more blurred image. Moreover, it is combined with the characteristics of the crack image itself. By calculating this metric and comparing it with other EAWD values from the same data set, the crack images are judged to be blurred or not. Furthermore, a support vector machine classifier is applied to the aforementioned metrics, serving as the image blur quality evaluator. For proper training and assessment of the proposed approach, an image data set consisting of 1,200 crack images is created, which also contains some thin crack images. Several experimental results are provided in this paper to demonstrate that the proposed method is fast, accurate, and reliable. | |
