{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:07:56Z","timestamp":1787029676919,"version":"build-2736575974"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Bureau of Changchun, China","award":["2024GD03"],"award-info":[{"award-number":["2024GD03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In current road segmentation tasks, high-frequency details of roads (such as road edges and pavement textures) tend to become blurred or even lost during feature extraction due to progressive downsampling, leading to imprecise segmentation boundaries. Moreover, existing fusion methods predominantly rely on simple concatenation or summation operations, which struggle to adaptively integrate the rich texture information from RGB modality with the geometric structural information from Depth modality, thereby limiting fusion efficiency. To address these issues, this paper proposes an innovative model. We design a Cross-scale Wavelet Enhancement Module (CWEM) to compensate for the shortcomings of traditional networks in frequency domain analysis, explicitly enhancing the representation capability of edge and texture features. Simultaneously, a Gated Cross-Modality Fusion module (GCMF) is constructed to achieve adaptive and efficient fusion between RGB and Depth features. Additionally, to tackle the high false detection rates and confusion between sidewalks and opposite lanes in existing methods, this paper optimizes the loss function to further improve the model\u2019s discriminative ability in complex scenarios. Experiments on the public KITTI_Road dataset demonstrate that the proposed method achieves a segmentation accuracy of 97.31% while maintaining a real-time inference speed of 34 FPS, with particularly outstanding performance in road edge integrity and shadow area handling.<\/jats:p>","DOI":"10.3390\/s26010218","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:33:25Z","timestamp":1767191605000},"page":"218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["WG-FuseNet: Wavelet-Guided and Gated Fusion Network for Road Segmentation"],"prefix":"10.3390","volume":"26","author":[{"given":"Yu","family":"Nie","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Sun","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8996-6834","authenticated-orcid":false,"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfu","family":"Yuan","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"Unit 94259 of the Chinese People\u2019s Liberation Army, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiarong","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9909","DOI":"10.1109\/TITS.2024.3359242","article-title":"Exploiting Low-Level Representations for Ultra-Fast Road Segmentation","volume":"25","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, J.-Y., Kim, S.-W., Lee, S.-W., Kim, Y.-W., and Ko, S.-J. (2019, January 27\u201328). Reverse and boundary attention network for road segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00116"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Garnett, N., Silberstein, S., Oron, S., Fetaya, E., Verner, U., Ayash, A., Goldner, V., Cohen, R., Horn, K., and Levi, D. (2017, January 21\u201326). Real-time category-based and general obstacle detection for autonomous driving. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/ICCVW.2017.32"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gu, S., Yang, J., and Kong, H. (June, January 30). A cascaded LiDAR-camera fusion network for road detection. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561935"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep learning for 3d point clouds: A survey","volume":"43","author":"Guo","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_8","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_9","first-page":"823","article-title":"Mask matching transformer for few-shot seg-mentation","volume":"35","author":"Zhang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, H., Gao, B., Liu, S., and Liu, Z. (2024, January 8\u201310). Semantic Segmentation of Road Landscape Based on Improved Deeplabv3+. Proceedings of the 2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Wuhu, China.","DOI":"10.1109\/ICAICE63571.2024.10864083"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/TCSI.2020.3038139","article-title":"RoadNet-RT: High throughput CNN architecture and SoC design for real-time road seg-mentation","volume":"68","author":"Bai","year":"2021","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., and Pv-rcnn, H.L. (2020, January 13\u201319). Point-voxel feature set abstraction for 3d object detection. Proceedings of the CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JAS.2019.1911459","article-title":"Progressive lidar adaptation for road detection","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin. (JAS)"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gao, Z., Wang, Q., Pan, Z., Zhai, Z., and Long, H. (2023). Pointpainting: 3d object detection aided by semantic image infor-mation. Sensors, 23.","DOI":"10.3390\/s23052868"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Quan, T.M., Hildebr, D.G.C., and Jeong, W.K. (2021). Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics. Front. Comput. Sci., 3.","DOI":"10.3389\/fcomp.2021.613981"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gu, J., Bellone, M., Pivo\u0148ka, T., and Sell, R. (2024). Clft: Camera-lidar fusion transformer for semantic segmentation in autonomous driving. arXiv.","DOI":"10.1109\/TIV.2024.3454971"},{"key":"ref_17","unstructured":"Guo, S., Wen, T., Liu, C., Chen, Q., and Fan, R. (2025). Fully Exploiting Vision Foundation Model\u2019s Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, Y., Hua, G., Long, R., Tian, S., and Zou, W. (2023, January 27\u201329). SPNet: An RGB-D sequence progressive network for road semantic segmentation. Proceedings of the 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Poitiers, France.","DOI":"10.1109\/MMSP59012.2023.10337660"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fan, R., Wang, H., Cai, P., and Liu, M. (2020). SNE-RoadSeg: Incorporating surface normal information into semantic segmentation for accurate freespace detection. European Conference on Computer Vision (ECCV), Springer International Publishing.","DOI":"10.36227\/techrxiv.12864287.v1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chang, Y., Xue, F., Sheng, F., Liang, W., and Ming, A. (2022, January 23\u201327). Fast road segmentation via uncertainty-aware symmetric network. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812452"},{"key":"ref_21","first-page":"5500505","article-title":"Wavemamba: Spatial-spectral wavelet mamba for hyperspectral image classification","volume":"22","author":"Ahmad","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s44443-025-00106-2","article-title":"Medical image segmentation network based on multi-scale cross-attention and wavelet transform","volume":"37","author":"Qin","year":"2025","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1134\/S1054661823020074","article-title":"Reducing the computational com-plexity of image processing using wavelet transform based on the Winograd method","volume":"33","author":"Lyakhov","year":"2023","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_24","first-page":"3","article-title":"The Winograd schema challenge","volume":"2012","author":"Levesque","year":"2012","journal-title":"KR"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105674","DOI":"10.1109\/ACCESS.2024.3434531","article-title":"WMANet: Wavelet-based multi-scale attention network for low-light image enhancement","volume":"12","author":"Xiang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s12530-022-09479-5","article-title":"RGB-D indoor semantic segmentation network based on wavelet transform","volume":"14","author":"Fan","year":"2023","journal-title":"Evol. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"53392","DOI":"10.1109\/ACCESS.2021.3070809","article-title":"Efficient attention fusion network in wavelet domain for demoireing","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7526","DOI":"10.1109\/TCSVT.2025.3549459","article-title":"WaveFusion: A Novel Wavelet Vision Transformer with Salien-cy-Guided Enhancement for Multimodal Image Fusion","volume":"35","author":"Wang","year":"2025","journal-title":"IEEE Trans. Circuis Syst. Video Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, Y., Zhan, F., Lu, S., Pan, J., Ma, F., Xie, X., and Miao, C. (2021, January 10\u201317). Wavefill: A wavelet-based generation network for image inpainting. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01385"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Peng, S., Zhang, T., Gao, L., Zhu, X., Zhang, H., Pang, K., and Lei, Z. (2025). Wmamba: Wavelet-based mamba for face forgery detection. arXiv.","DOI":"10.1145\/3746027.3755592"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5005416","DOI":"10.1109\/TGRS.2025.3584385","article-title":"Irsrmamba: Infrared image super-resolution via mamba-based wavelet transform feature modulation model","volume":"63","author":"Huang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/JSTSP.2022.3181717","article-title":"IGNFusion: An unsupervised information gate network for mul-timodal medical image fusion","volume":"16","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, X., Lin, K.Y., Wang, J., Wu, W., Qian, C., Li, H., and Zeng, G. (2020). Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation. European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-58621-8_33"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_35","first-page":"3926","article-title":"CFM: Convolutional factorization machines for context-aware recommendation","volume":"19","author":"Xin","year":"2019","journal-title":"IJCAI"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, C., He, Y., Li, X., and Jing, X. (2019, January 23\u201327). BiGRU network for human activity recognition in high resolution range profile. Proceedings of the 2019 International Radar Conference (RADAR), Toulon, France.","DOI":"10.1109\/RADAR41533.2019.171259"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, X., and Zhou, H. (2025). WDM-UNet: A Wavelet-Deformable Gated Fusion Network for Multi-Scale Retinal Vessel Seg-mentation. Sensors, 25.","DOI":"10.3390\/s25154840"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fritsch, J., K\u00fchnl, T., and Geiger, A. (2013, January 6\u20139). A new performance measure and evaluation benchmark for road detection algorithms. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728473"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Min, C., Jiang, W., Zhao, D., Xu, J., Xiao, L., Nie, Y., and Dai, B. (2022, January 23\u201327). Orfd: A dataset and benchmark for off-road freespace detection. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812139"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.robot.2018.11.002","article-title":"LiDARcamera fusion for road detection using fully convolutional neural networks","volume":"111","author":"Caltagirone","year":"2019","journal-title":"Robot. Auton. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:35:36Z","timestamp":1767191736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26010218"],"URL":"https:\/\/doi.org\/10.3390\/s26010218","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]}}}