[1] Phi T T, Kulatilake P H S W, Ankah M L Y et al. Rock mass statistical homogeneity investigation along a highway corridor in Vietnam[J]. Eng Geol, 289, 106176(2021).

[2] Yan K, Zhang Z H. Automated asphalt highway pavement crack detection based on deformable single shot multi-box detector under a complex environment[J]. IEEE Access, 9, 150925-150938(2021).

[3] Zhang X. Highway crack material detection algorithm based on digital image processing technology[J]. J Phys Conf Ser, 2425, 012067(2023).

[4] Weng X X, Huang Y C, Li Y N et al. Unsupervised domain adaptation for crack detection[J]. Autom Constr, 153, 104939(2023).

[5] Yang Y T, Mei G. Deep transfer learning approach for identifying slope surface cracks[J]. Appl Sci, 11, 11193(2021).

[6] Huang Z K, Chang D D, Yang X F et al. A deep learning-based approach for crack damage detection using strain field[J]. Eng Fract Mech, 293, 109703(2023).

[7] Zhu X K, Lyu S C, Wang X et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C], 2778-2788(2021). https://doi.org/10.1109/ICCVW54120.2021.00312

[8] Li C Y, Li L L, Jiang H L et al. YOLOv6: a single-stage object detection framework for industrial applications[Z](2022). https://arxiv.org/abs/2209.02976

[9] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C], 7464-7475(2023). https://doi.org/10.1109/CVPR52729.2023.00721

[10] Gong L X, Huang X, Chao Y K et al. Correction to: an enhanced SSD with feature cross-reinforcement for small-object detection[J]. Appl Intell, 53, 21483-21484(2023).

[11] Liu X, Mo S T, Zhang J et al. PeleeNet_yolov3 surface crack identification with lightweight model[J]. J Harbin Inst Technol, 55, 81-89(2023).

[12] Zhou Z, Zhang J J, Lu S P. Tunnel lining crack detection algorithm based on improved YOLOv4[J]. J China Railway Soc, 45, 162-170(2023).

[13] Hu Q F, Wang P, Li S M et al. Research on intelligent crack detection in a deep-cut canal slope in the Chinese South–North water transfer project[J]. Remote Sens, 14, 5384(2022).

[14] Chen X, Peng D L, Gu Y. Real-time object detection for UAV images based on improved YOLOv5s[J]. Opto-Electron Eng, 49, 210372(2022).

[15] Zhao C, Shu X, Yan X et al. RDD-YOLO: a modified YOLO for detection of steel surface defects[J]. Measurement, 214, 112776(2023).

[16] Ni C S, Li L, Luo W T et al. Disease detection of asphalt pavement based on improved YOLOv7[J]. Comput Eng Appl, 59, 305-316(2023).

[17] Liang L M, Long P W, Lu B H et al. Improvement of GBS-YOLOv7t for steel surface defect detection[J]. Opto-Electron Eng, 51, 240044(2024).

[18] Xu X Q, Li Q, Li S E et al. Crack width recognition of tunnel tube sheet based on YOLOv8 algorithm and 3D imaging[J]. Buildings, 14, 531(2024).

[19] Talaat F M, Zaineldin H. An improved fire detection approach based on YOLO-v8 for smart cities[J]. Neural Comput Appl, 35, 20939-20954(2023).

[20] Beyene D A, Tran D Q, Maru M B et al. Unsupervised domain adaptation-based crack segmentation using transformer network[J]. J Build Eng, 80, 107889(2023).

[21] Wang A, Chen H, Lin Z J et al. Rep ViT: revisiting mobile CNN from ViT perspective[C](2024). https://doi.org/10.1109/CVPR52733.2024.01506

[22] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2019). https://doi.org/10.1109/ICCV.2019.00140

[23] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Trans Pattern Anal Mach Intell, 42, 2011-2023(2020).

[24] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020). https://doi.org/10.1109/CVPR42600.2020.00165

[25] Chen Y P, Dai X Y, Liu M C et al. Dynamic convolution: attention over convolution kernels[C], 1030-11039(2020). https://doi.org/10.1109/CVPR42600.2020.01104

[26] Han K, Wang Y H, Guo J Y et al. ParameterNet: parameters are all you need for large-scale visual pretraining of mobile networks[C], 15751-15761(2024). https://doi.org/10.1109/CVPR52733.2024.01491

[27] Wu Y X, He K M. Group normalization[C], 3-19(2018). https://doi.org/10.1007/978-3-030-01261-8_1

[28] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017). https://doi.org/10.1109/CVPR.2017.106

[29] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[C], 12993-13000(2020). https://doi.org/10.1609/aaai.v34i07.6999

[30] Gevorgyan Z. SIoU loss: more powerful learning for bounding box regression[Z](2022). https://arxiv.org/abs/2205.12740

[31] Arya D, Maeda H, Ghosh S K et al. RDD2022: a multi‐national image dataset for automatic road damage detection[Z](2022). https://arxiv.org/abs/2209.08538

[32] Zhao Y, Lv W Y, Xu S L et al. DETRs beat YOLOs on real-time object detection[C], 16965-16974(2024). https://doi.org/10.1109/CVPR52733.2024.01605

[33] Shi Y G, Li S K, Liu Z Y et al. MTP-YOLO: you only look once based maritime tiny person detector for emergency rescue[J]. J Mar Sci Eng, 12, 669(2024).

[34] Woo S, Debnath S, Hu R H et al. ConvNeXt V2: co-designing and scaling ConvNets with masked autoencoders[C], 16133-16142(2023). https://doi.org/10.1109/CVPR52729.2023.01548

[35] Subramanyam A V, Singal N, Verma V K. Resource efficient perception for vision systems[Z](2024). https://arxiv.org/abs/2405.07166

[36] Zhang Y F, Ren W Q, Zhang Z et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 506, 146-157(2022).

[37] Tong Z J, Chen Y H, Xu Z W et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[Z](2023). https://arxiv.org/abs/2301.10051