###### Abstract  
As the expected lifespans of structures and road approaches, as well as the importance of road maintenance, increase globally, safety inspections have emerged as a crucial task. Nonetheless, the existing crack detection models focus on multi-scale feature loss and performance degradation in learning various types of cracks. We propose the Multi-Scale Parallel Attention U-Net (MSP U-Net) as a network designed for low-resolution images that considers the irregular characteristics of cracks. MSP U-Net applies a large receptive field flock to an attention U-Net, minimizing feature loss across multiple scales. Using the Crack500 dataset, our network achieved a mean intersection of union (mIoU) of 0.7752, outperforming the existing methods on low-resolution images. c pacs: 02.40.-a, 02.40.-a, 02. Spatial and Channel Squeeze and Excitation (SCSE) modules [8]. [PERSON] et al. [9] applied preprocessing techniques suitable for deep-learning-based networks rather than digital image processing techniques used in traditional computer vision tasks, showing good accuracy and model stability. [PERSON] et al. [10] proposed a semantic segmentation network called RUC-Net that combines ResNet-18 with U-Net. They applied focal loss functions to address class imbalance issues, reduced sample weights, and demonstrated better detection performance than classical segmentation algorithms. [PERSON] et al. [11] proposed U-Net with DenseNet121 as an encoder based on a Pyramid Attention Network (PAN). They applied feature pyramid attention modules between the encoder and decoder to extract high-level features for classification and segmentation. The decoder module achieved good performance by accurately combining features from both low and high dimensions using the Global Attention Upsample (GAU) module. [PERSON] et al. [12] designed a U-Net-based architecture using a ResNet-50 encoder. Through transfer learning, the proposed architecture outperformed the existing methods using a common dataset. [PERSON] et al. [13] proposed a network called SDDNet by applying a purely deep learning method. This network can perform real-time crack detection while ignoring backgrounds that resemble crack surfaces. Despite an 88-fold reduction in parameters compared to the latest models at the time of publication, SDDNet achieved better performance and faster processing speed than said models. [PERSON] et al. [14] proposed STRNet, which excels at segmenting concrete cracks at the pixel level. The network was trained using a large-scale dataset and tested on 545 images. The performance-recorded Precision, Recall, F-1 Score, and mIoU were 91.7%, 92.7%, 92.2%, and 92.6%, respectively. The proposed network has the advantage of processing relatively large input images (1280 \(\times\) 720 and 1024 \(\times\) 512) in real-time while maintaining excellent performance. [PERSON] et al. [15] proposed a road surface crack detection technique using fully convolutional neural networks (FCNNs). They segmented images obtained while driving on actual roads, with a total of 6756 images, and classified pavement damage into six classes. Because the dataset was initially insufficient, it was stabilized using K-fold cross-validation. Furthermore, they applied a semi-supervised learning-based method using pseudo-label datasets, outperforming conventional supervised-learning-based networks. Although the aforementioned studies have been somewhat successful in improving crack detection performance, they still exhibited suboptimal accuracy when handling low-resolution images. One of the main reasons for this is the loss of features in certain layers of the encoder in an autoencoder-based structure. Drawing inspiration from this, we propose a network that achieves improved detection performance with low-resolution images. The contributions of the proposed network are summarized as follows:  
First, we focused on the loss of features occurring in certain network layers as a major cause of degradation in detection performance.