A Nested Unet with Attention Mechanism for Road Crack Image Segmentation

Road crack detection is the key link of road maintenance. Only when the cracks are detected can they be repaired. Since cracks are not common and manual detection efficiency is low, inspectors prefer to use machine vision methods for crack detection. Aiming at the problems of high noise, low precision and easy loss of image details in traditional road crack detection, this paper improves the classic image segmentation model Unet and applies it to road crack detection. The new model changes Unet to nested structure and integrates attention mechanism on this basis. The nested Unet can better fuse feature maps from different layers through skip connections and retain the details of road crack images effectively. And the attention mechanism is introduced to suppress the noise in irrelevant regions. The improved model has been evaluated on an expanded road crack dataset containing 9,990 images. According to the experimental results, the model can significantly eliminate noise, improve segmentation accuracy, and retain crack details.