Aiming at the problems of low detection accuracy and slow speed of the traditional method for detecting surface cracks on ballastless track slabs of high-speed railways, an improved CenterNetbased algorithm for detecting surface cracks on track slabs is proposed. The algorithm adds atrous space pyramid pooling module (ASPP) between the codec network as a way to expand the perceptual field of the feature map and fully extract the contextual information at different scales. Then adds a multispectral channel attention module (MCA) to the feature extraction network so that the network can better learn the weights of each channel and capture the image rich input feature information. Finally, the αIoU loss function is used to improve the accuracy of bounding box prediction. The experimental results show that the mean average precision(mAP) of the proposed algorithm reaches 8412%, which is 337% higher than that of the traditional algorithm, and it has a good detection effect on the surface cracks of the track plate.