Pavement Image Enhancement in Pixel-Wise Based on Multi-Level Semantic Information

The detection of sealed cracks in pavement images can be complicated by the presence of objects on the pavement that have similar morphology or texture to cracks, leading to false positive results. To address this issue, this paper proposes a High Resolution-Pixel to Pixel (HR-Pix2Pix) pavement distress image enhancement algorithm. The algorithm improves the proportion of effective semantics by reducing the semantic interference caused by pseudo pavement distress such as pavement stains, brake marks, and shadows. The proposed HR-Pix2Pix generative adversarial neural network involves a novel generator architecture for fusing multi-level semantic features. The generator is capable to automatically locate the pseudo-distress in the image and fill (generate) the region as normal pavement texture with reference to the pavement texture surrounding the pseudo-distress. To further improve the quality of the semantic distribution-enhanced images, a hybrid loss function is designed and used to train the network. The results of the study show that by using HR-Pix2Pix enhanced images for sealed crack detection can significantly reduce false detections and improve the accuracy of the detection, consequently.