Efficient image segmentation method for interface heterogeneous materials of grouted asphalt concrete under small sample conditions

BAI Tao; ZHAN Ao-yang; GU Fan

doi:10.19818/j.cnki.1671-1637.2026.008

Volume 26 Issue 2

Feb. 2026

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Article Navigation > Journal of Traffic and Transportation Engineering > 2026 > 26(2): 225-242

BAI Tao, ZHAN Ao-yang, GU Fan. Efficient image segmentation method for interface heterogeneous materials of grouted asphalt concrete under small sample conditions[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 225-242. doi: 10.19818/j.cnki.1671-1637.2026.008

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BAI Tao, ZHAN Ao-yang, GU Fan. Efficient image segmentation method for interface heterogeneous materials of grouted asphalt concrete under small sample conditions[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 225-242. doi: 10.19818/j.cnki.1671-1637.2026.008

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Efficient image segmentation method for interface heterogeneous materials of grouted asphalt concrete under small sample conditions

doi: 10.19818/j.cnki.1671-1637.2026.008

BAI Tao^1
,,
ZHAN Ao-yang¹,
GU Fan^{2
,
,}

1.
School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430205, Hubei, China
2.
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China

Funds:

National Nature Science Foundation of China 52578528

National Nature Science Foundation of China 52378438

Science and Technology Project of Department of Transportation of Hubei Province 2023-121-1-32

More Information

Corresponding author: GU Fan, professor, PhD, E-mail: fan.gu@csust.edu.cn
Received Date: 2025-02-28
Accepted Date: 2025-08-25
Rev Recd Date: 2025-07-02
Publish Date: 2026-02-28

Abstract

Abstract

An intelligent segmentation framework coupling physical constraints and attention mechanisms was proposed. An adaptive erosion-dilation preprocessing algorithm was proposed, combining with multi-weight edge fusion operators to enhance gradient responses of interfaces. The atrous spatial pyramid pooling-squeeze and excitation (ASPP-SE) network architecture was designed to achieve multi-scale feature decoupling of pore distribution and aggregate texture through synergistic optimization of SE-Block and ASPP. A joint correction strategy integrating edge confidence and segmentation masks was established to optimize initial segmentation results through post-processing. The results demonstrate that on our self-constructed small-sample dataset of grouted asphalt concrete, the proposed method achieves an improvement of 8.4% in accuracy, 6.6% in precision, and 6.9% increase in recall compared to the SegFormer deep learning model, effectively enhancing boundary segmentation accuracy. This method effectively resolves segmentation failures in complex scenarios including blurred material phase interfaces and aggregate texture features, demonstrating superior reliability and generalizability in practical engineering applications. It also provides new insights for analyzing engineering material images with limited data and high heterogeneity.
- road engineering,
- grouted asphalt concrete,
- deep learning,
- digital image processing,
- edge detection,
- image segmentation,
- computer vision

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References(45)

References

[1]	ZHAO W T, YANG Q. Study on the applicability of asphalt concrete skeleton in the semi-flexible pavement[J]. Construction and Building Materials, 2022, 327: 126923. doi: 10.1016/j.conbuildmat.2022.126923
[2]	LIU Zhi-yang, DONG Ze-jiao, ZHOU Tao, et al. Review and prospects of performance enhancement of asphalt mixtures based on material informatics[J]. China Journal of Highway and Transport, 2024, 37(4): 98-120.
[3]	BAI T, CHENG Y X, LI Y Y, et al. Design optimization and performance evaluation of metakaolin based geopolymer filled porous (semi-flexible) asphalt mixture[J]. Construction and Building Materials, 2023, 408: 133611. doi: 10.1016/j.conbuildmat.2023.133611
[4]	DENG Cheng, HUANG Chong, HONG Jin-xiang, et al. The study of super-early-strength semi-flexible pavement used in municipal road[J]. Highway Engineering, 2016, 41(1): 116-119, 138.
[5]	CAI Xing. Macro-mesoscopic study on mechanical behavior of grouted semi-flexible pavement material[D]. Nanjing: Southeast University, 2021.
[6]	CHEN Y W, BAI T, ZHAN A Y, et al. A study of fine-scale low-temperature cracking in geopolymer grouted porous asphalt mixtures based on real aggregate profile modeling[J]. Construction and Building Materials, 2024, 445: 137897. doi: 10.1016/j.conbuildmat.2024.137897
[7]	CUI Zhe, ZHANG Sheng-rui. Computational method of 3D aggregate angularity based on CT images[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 39-49. https://transport.chd.edu.cn/article/id/201705004
[8]	FANG X, WANG C, LI H, et al. Influence of mesoscopic pore characteristics on the splitting-tensile strength of cellular concrete through deep-learning based image segmentation[J]. Construction and Building Materials, 2022, 315: 125335. doi: 10.1016/j.conbuildmat.2021.125335
[9]	YU Y X, ZHENG N, QIAO T, et al. Distinguishing between natural and recolored images via lateral chromatic aberration[J]. Journal of Visual Communication and Image Representation, 2021, 80: 103295. doi: 10.1016/j.jvcir.2021.103295
[10]	WANG Z Q, XIE J G, GAO L, et al. Study on air void characteristics and hydraulic characteristics of porous asphalt concrete based on image processing technology[J]. Geofluids, 2021, 2021: 9432323.
[11]	NOCK R, NIELSEN F. Statistical region merging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1452-1458. doi: 10.1109/TPAMI.2004.110
[12]	KASS M, WITKIN A, TERZOPOULOS D. Snakes: Active contour models[J]. International Journal of Computer Vision, 1988, 1(4): 321-331. doi: 10.1007/BF00133570
[13]	BOYKOV Y, VEKSLER O, ZABIH R. Fast approximate energy minimization via graph cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239. doi: 10.1109/34.969114
[14]	ZHAO W X, WANG W W, FENG X C, et al. A new variational method for selective segmentation of medical images[J]. Signal Processing, 2022, 190: 108292. doi: 10.1016/j.sigpro.2021.108292
[15]	STARCK J L, ELAD M, DONOHO D L. Image decomposition via the combination of sparse representations and a variational approach[J]. IEEE Transactions on Image Processing, 2005, 14(10): 1570-1582. doi: 10.1109/TIP.2005.852206
[16]	MINAEE S, WANG Y. An ADMM approach to masked signal decomposition using subspace representation[J]. IEEE Transactions on Image Processing, 2019, 28(7): 3192-3204. doi: 10.1109/TIP.2019.2894966
[17]	LI C L, LI Y G, HAN Z, et al. A novel multiphase segmentation method for interpreting the 3D mesoscopic structure of asphalt mixture using CT images[J]. Construction and Building Materials, 2022, 327: 127010. doi: 10.1016/j.conbuildmat.2022.127010
[18]	LI X L, LV X C, ZHOU Y H, et al. Homogeneity evaluation of hot in-place recycling asphalt mixture using digital image processing technique[J]. Journal of Cleaner Production, 2020, 258: 120524. doi: 10.1016/j.jclepro.2020.120524
[19]	MEJÍA A M, ALZATE M A, REYES-ORTIZ O J. Segmentation of aggregate and asphalt in photographic images of pavements[J]. Engineering Transactions, 2021, 69(1): 19-42.
[20]	ENRÍQUEZ-LEÓN A J, DE SOUZA T D, ARAGÃO F T S, et al. Determination of the air void content of asphalt concrete mixtures using artificial intelligence techniques to segment micro-CT images[J]. International Journal of Pavement Engineering, 2022, 23(11): 3973-3982. doi: 10.1080/10298436.2021.1931197
[21]	LIU Z J, HUANG T, LIU G Q. Applicability of a new method for mesoscopic structure segmentation of asphalt mixture based on two-dimensional image[J]. Construction and Building Materials, 2024, 421: 135738. doi: 10.1016/j.conbuildmat.2024.135738
[22]	WEI J J, LI H B, WAN C. X-ray CT image segmentation of asphalt concrete based on fuzzy C means[J/OL]. Applied Mechanics and Materials, 2012, 170/171/172/173: 3444-3448.
[23]	HUYAN J, MA T, LI W, et al. Pixelwise asphalt concrete pavement crack detection via deep learning-based semantic segmentation method[J]. Structural Control and Health Monitoring, 2022, 29(8): e2974.
[24]	HAN S S, CHEN J, JIANG Y H. Experimental evaluation of convolutional neural networks in asphalt concrete computed tomography scan image analysis[C]//ASCE. Construction Research Congress 2024. Reston: ASCE, 2024: 506-515.
[25]	PENG Y, YANG H D. Aggregate boundary recognition of asphalt mixture CT images based on convolutional neural networks[J]. Road Materials and Pavement Design, 2024, 25(5): 1127-1143. doi: 10.1080/14680629.2023.2233630
[26]	DONG J X, LIU J H, WANG N N, et al. Intelligent segmentation and measurement model for asphalt road cracks based on modified mask R-CNN algorithm[J]. Computer Modeling in Engineering Sciences, 2021, 128(2): 541-564. doi: 10.32604/cmes.2021.015875
[27]	LIU Y Y, BAI X T, WANG J F, et al. Image semantic segmentation approach based on DeepLabV3 plus network with an attention mechanism[J]. Engineering Applications of Artificial Intelligence, 2024, 127: 107260. doi: 10.1016/j.engappai.2023.107260
[28]	HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//IEEE. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 7132-7141.
[29]	CHEN Y T, TAO J J, LIU L W, et al. Research of improving semantic image segmentation based on a feature fusion model[J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 13(11): 5033-5045. doi: 10.1007/s12652-020-02066-z
[30]	WANG Z H, WU L. Theoretical analysis of the inductive biases in deep convolutional networks[C]//Curran Associates. 37th Conference on Neural Information Processing Systems, NeurIPS 2023. New Orleans: Curran Associates, 2022: 74289-74338.
[31]	HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//IEEE. 2017 IEEE International Conference on Computer Vision (ICCV). New York: IEEE, 2017: 2980-2988.
[32]	ARCHANA R, ELIAHIM JEEVARAJ P S. Deep learning models for digital image processing: A review[J]. Artificial Intelligence Review, 2024, 57(1): 11. doi: 10.1007/s10462-023-10631-z
[33]	LI P P, LIU Z P. GeoBind: Segmentation of nucleic acid binding interface on protein surface with geometric deep learning[J]. Nucleic Acids Research, 2023, 51(10): e60. doi: 10.1093/nar/gkad288
[34]	XU X M, TU W P, YANG Y H. CASE-Net: Integrating local and non-local attention operations for speech enhancement[J]. Speech Communication, 2023, 148: 31-39. doi: 10.1016/j.specom.2023.02.006
[35]	PARMAR N, VASWANI A, USZKOREIT J, et al. Image transformer[C]//Microtome Publishing. International Conference on Machine Learning. New York: PMLR, 2018: 4055-4064.
[36]	ZHAO H S, JIA J Y, KOLTUN V. Exploring self-attention for image recognition[C]//IEEE. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2020: 10073-10082.
[37]	SHORTEN C, KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6(1): 60. doi: 10.1186/s40537-019-0197-0
[38]	WANG C H, HUANG K Y, YAO Y, et al. Lightweight deep learning: an overview[J]. IEEE Consumer Electronics Magazine, 2024, 13(4): 51-64. doi: 10.1109/MCE.2022.3181759
[39]	YANG En-hui, CHEN Qiang, LI Jie, et al. Surface texture reconstruction and mean texture depth prediction model of asphalt pavement[J]. China Journal of Highway and Transport, 2023, 36(6): 14-23.
[40]	GAO Tao, XING Ke, LIU Zhan-wen, et al. Traffic sign detection algorithm based on pyramid multi-scale fusion[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 210-224. doi: 10.19818/j.cnki.1671-1637.2022.03.017
[41]	KAJKAMHAENG S, CHANTRAPORNCHAI C. SE-SqueezeNet: SqueezeNet extension with squeeze and-excitation block[J]. International Journal of Computational Science and Engineering, 2021, 24(2): 185. doi: 10.1504/IJCSE.2021.115105
[42]	CHENG B W, MISRA I, SCHWING A G, et al. Masked-attention mask transformer for universal image segmentation[C]//IEEE. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2022: 1280-1289.
[43]	LI Z, WANG M, MEI J, et al. Mail: A unified mask-image-language trimodal network for referring image segmentation[J/OL]. 2021, https://doi.org/10.48550/arXiv.2111.10747.
[44]	YU Hua-nan, YAO Ding, QIAN Guo-ping, et al. Review of digital twin model of asphalt mixture performance based on mesostructure characteristics[J]. China Journal of Highway and Transport, 2023, 36(3): 20-44.
[45]	QIN D F, LEICHNER C, DELAKIS M, et al. MobileNetV4: Universal models for the mobile ecosystem[C]//Springer. Computer Vision-ECCV 2024. Berlin: Springer, 2024: 78-96.

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Efficient image segmentation method for interface heterogeneous materials of grouted asphalt concrete under small sample conditions

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Efficient image segmentation method for interface heterogeneous materials of grouted asphalt concrete under small sample conditions

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