With the increase in global vehicles and the expansion of road surfaces, pavement crack detection has received extensive attention in recent years. Many detector models have been proposed, with some problems, though. For example, some narrow cracks may not be detected, leading to discontinuous cracks; the detailed crack edge information may be lost during filtering or pooling. On the basis of SegNet, a continuous attention mechanism is designed in the encoder layer, and a convolutional pyramid structure is added before the feature map passes through the decoder layer to reduce the fracture in crack detection and obtain more complete edge information. The Precision, Recall, and F1-measure of our approach are 2.47%, 8.21%, and 6.87% higher than those of the related method, respectively, and the Mean Intersection over Union (MIoU) of the detection results on the three open datasets, namely, Crack200, Crack500, and CrackForest is improved by 14.35%.