References
- Hao S, Shao L, Wang S. A Faster RCNN Airport Pavement Crack Detection Method Based on Attention Mechanism [J]. Academic Journal of Science and Technology, 2022, 4(2): 129-132.
- Redmon J, and Farhadi A. YOLOv3: An Incremental Improvement [J]. CoRR, 2018, 1804: 02767.
- Wu L, Duan Z, Liang C. Research on asphalt pavement disease detection based on improved YOLOv5s[J]. Journal of Sensors, 2023, 2023(1): 2069044.
- DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale [J]. arXiv preprint arXiv:2010.11929, 2020.
- CARIONN, MASSAF, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//Proceedings of the 2020 European Conference on Computer Vision. Cham: Springer International Publishing, 2020: 213-229.
- Zhu X, Su W, Lu L, et al. Deformable detr: Deformable transformers for end-to-end object detection[J/OL]. arXiv preprint arXiv, 2020[2024-11-18]. https://doi.org/10.48550/arXiv.2010.04159
- CHEN Q, CHENX, WANGJ, et al. Group detr: Fast detr training with group-wise one-to-many assignment [C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 6633-6642.
- Zhang H, Li F, Liu S, et al. Dino: Detr with improved denoising anchor boxes for end-to-end object detection [J]. arXiv preprint arXiv:2203.03605, 2022.
- Zong Z, Song G, Liu Y. Detrs with collaborative hybrid assignments training [C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 6748-6758.
- Chen Y, Zhang C, Chen B, et al. Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases[J]. Computers in Biology and Medicine, 2024, 170: 107917.
- Zhao Y, Lv W, Xu S, et al. Detrs beat yolos on real-time object detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 16965-16974.
- He K, Zhang X, Ren S, et al. Deep residual learning for image recognition [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
- Wang C Y, Yeh I H, Mark Liao H Y. Yolov9: Learning what you want to learn using programmable gradient information [C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2024: 1-21.
- Ding X, Zhang Y, Ge Y, et al. UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 5513-5524.
- Sunkara R, Luo T. No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects [C]//Joint European conference on machine learning and knowledge discovery in databases. Cham: Springer Nature Switzerland, 2022: 443-459.
- Cui Y, Ren W, Knoll A. Omni-Kernel Network for Image Restoration [C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(2): 1426-1434.
- Arya D, Maeda H, Ghosh S K, et al. RDD2020: An annotated image dataset for automatic road damage detection using deep learning [J]. Data in brief, 2021, 36: 107133.
- Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object classes (voc) challenge[J]. International journal of computer vision, 2010, 88: 303-338.
- Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1137-1149.
- Khanam R, Hussain M. Yolov11: An overview of the key architectural enhancements [J]. arXiv preprint arXiv:2410.17725, 2024.