[1] Ma J, Zhao X M, He S H et al. Review of pavement detection technology[J]. Journal of Traffic and Transportation Engineering, 17, 121-137(2017).

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

[3] Li P, Wang C, Li S M et al. Research on crack detection method of airport runway based on twice-threshold segmentation[C], 1716-1720(2016).

[4] Li P, Zhao F F, Du M. The cracks detection algorithm of pavement based on histogram of dual-tree complex wavelet[J]. Journal of Anhui University (Natural Science Edition), 42, 38-44(2018).

[5] Han K, Han H F. Pavement crack detection method based on region-level and pixel-level features[J]. Journal of Railway Science and Engineering, 15, 1178-1186(2018).

[6] Zhao F, Deng Y J. Light dim small target detection network with multi-heterogeneous filters[J]. Acta Optica Sinica, 43, 0915001(2023).

[7] Lin J P, Haberstroh F, Karsch S et al. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 11, e7(2023).

[8] Chen B, He S, Liu J et al. Weld structured light image segmentation based on lightweight DeepLab v3+ network[J]. Chinese Journal of Lasers, 50, 0802105(2023).

[9] Gao H, Yan X D, Zhang H et al. Multi-scale sea-land segmentation method for remote sensing images based on Res2Net[J]. Acta Optica Sinica, 42, 1828004(2022).

[10] Luo H, Jia C, Li J. Road surface disease detection algorithm based on improved YOLOv4[J]. Laser & Optoelectronics Progress, 58, 1410025(2021).

[11] Hacıefendioğlu K, Başağa H B. Concrete road crack detection using deep learning-based faster R-CNN method[J]. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46, 1621-1633(2022).

[12] Sun Z Y, Ma Z D, Li W et al. Pavement crack identification method based on deep convolutional neural network fusion model[J]. Journal of Chang’an University (Natural Science Edition), 40, 1-13(2020).

[13] Peng L, Zhang H. U-Net for pavement crack detection[J]. Computer Science, 48, 616-619(2021).

[14] Wang B X, Bai S X, Zhao W G. Pavement crack damage visual detection method based on feature reinforcement learning[J]. Journal of Railway Science and Engineering, 19, 1927-1935(2022).

[15] Yuan J H, Zhang W F, Yue X J et al. Semantic Segmentation of concrete beam cracks based on DeepLabv3+ in Multiple Backbone Networks[C], 160-164(2021).

[16] Li G Y, Liang J D, Liu Y et al. MFC-DeepLabV3+: a multi feature cascade fusion crack defect detection network model[J]. Journal of Railway Science and Engineering, 20, 1370-1381(2023).

[17] Yang F, Zhang L, Yu S J et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 21, 1525-1535(2020).

[18] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2020).

[19] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[20] Hou Q B, Zhang L, Cheng M M et al. Strip pooling: rethinking spatial pooling for scene parsing[C], 4002-4011(2020).

[21] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

[22] Nguyen N T H, Le T H, Perry S et al. Pavement crack detection using convolutional neural network[C], 251-256(2018).

[23] Cao J G, Yang G T, Yang X Y. Pavement crack detection with deep learning based on attention mechanism[J]. Journal of Computer-Aided Design & Computer Graphics, 32, 1324-1333(2020).

[24] Wang W J, Su C. Convolutional neural network-based pavement crack segmentation using pyramid attention network[J]. IEEE Access, 8, 206548-206558(2020).