[1] Zhang J, Sha A M, Gao H G et al. Automatic pavement crack recognition and evaluation system based on digital image processing[J]. Journal of Chang’an University (Natural Science Edition), 24, 18-22(2004).

[2] Bai Z B, Zhang J J, Gao Y et al. Attention mechanism-based object detection algorithm in aerial images[J]. Laser & Optoelectronics Progress, 60, 1215003(2023).

[3] Zhou Y, Liu T. Computer vision-based crack detection and measurement on concrete structure[J]. Journal of Tongji University (Natural Science), 47, 1277-1285(2019).

[4] Kim H, Sim S, Cho S. Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing[C], 194-199(2015).

[5] Wang B, Wang X, Chen F et al. Pavement crack recognition based on aerial image[J]. Acta Optica Sinica, 37, 0810004(2017).

[6] Li G, Gao Z Y, Zhang X C et al. Improved global convolutional network for pavement crack detection[J]. Laser & Optoelectronics Progress, 57, 081011(2020).

[7] Yang X C, Li H, Yu Y T et al. Automatic pixel‐level crack detection and measurement using fully convolutional network[J]. Computer-Aided Civil and Infrastructure Engineering, 33, 1090-1109(2018).

[8] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[9] Liu Z Q, Cao Y W, Wang Y Z et al. Computer vision-based concrete crack detection using U-net fully convolutional networks[J]. Automation in Construction, 104, 129-139(2019).

[10] Song C G, Wu L J, Chen Z C et al. Pixel-level crack detection in images using SegNet[M]. Multi-disciplinary trends in artificial intelligence, 11909, 247-254(2019).

[11] Zhu Y F, Wang H T, Li K et al. Crack U-net: towards high quality pavement crack detection[J]. Computer Science, 49, 204-211(2022).

[12] Zhang M X, Xu J, Liu X P et al. Improved U-Net pavement crack detection method[J/OL]. Computer Engineering and Applications, 1-10. https://kns.cnki.net/kcms/detail/11.2127.TP.20231008.1724.026.html

[13] Hong Z H, Yang F, Pan H Y et al. Highway crack segmentation from unmanned aerial vehicle images using deep learning[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6503405(2022).

[14] Feng S L, Zhao H, Shi F et al. CPFNet: context pyramid fusion network for medical image segmentation[J]. IEEE transactions on medical imaging, 39, 3008-3018(2020).

[15] Feng J, Bi J K, Huo Y R et al. PIPNet: a lightweight asphalt pavement crack image segmentation network[J], 44, 1520-1526(2024).

[16] Xia X H, Su J G, Wang Y Y et al. Lightweight pavement crack detection model based on DeepLabv3+[J]. Laser & Optoelectronics Progress, 61, 0812001(2024).

[17] Meng Q C, Li M J, Wan D et al. Real-time segmentation algorithm of concrete cracks based on M-Unet[J]. Journal of Civil and Environmental Engineering, 46, 215-222(2024).

[18] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2019).

[19] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

[20] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[21] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

[22] Xu S J, Hao M, Meng Y B et al. Crack detection method of holistically-nested network based on feature enhancement[J]. Laser & Optoelectronics Progress, 59, 1010003(2022).

[23] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. http://arxiv.org/abs/1706.05587v3