Abstract
Drones have emerged as a critical tool for the detection of high-altitude glass curtain cracks. However, their utility is often compromised by vibrations and other environmental factors that can induce motion blur, compromising image quality and the accuracy of crack detection. This paper presents a novel GAN-based and enhanced U-shaped Transformer network, named GlassCurtainCrackDeblurNet, designed specifically for the deblurring of drone-captured images of glass curtain cracks. To optimize the performance of our proposed method for this application, we have meticulously created the GlassCurtainCrackDeblur Dataset. Our method demonstrates superior qualitative and quantitative outcomes when compared to other established deblurring techniques on both the GoPro Dataset and the GlassCurtainCrackDeblur Dataset.