{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:17:10Z","timestamp":1788239830848,"version":"build-2803163510"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61472444"],"award-info":[{"award-number":["61472444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61671470"],"award-info":[{"award-number":["61671470"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["61472444"],"award-info":[{"award-number":["61472444"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["61671470"],"award-info":[{"award-number":["61671470"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Scientific and Technological Project of Henan Province","award":["262102210065"],"award-info":[{"award-number":["262102210065"]}]},{"name":"Scientific and Technological Project of Henan Province","award":["252102220131"],"award-info":[{"award-number":["252102220131"]}]},{"name":"Key Research and Development Projects in Henan Province","award":["261111221200"],"award-info":[{"award-number":["261111221200"]}]},{"name":"Key Research and Development Projects in Henan Province","award":["261111222300"],"award-info":[{"award-number":["261111222300"]}]},{"name":"Key Research and Development Projects in Henan Province","award":["251111220500"],"award-info":[{"award-number":["251111220500"]}]},{"name":"Water Conservancy Equipment and Intelligent Operation & Maintenance Engineering Technology Research Centre in Henan Province","award":["Yukeshi2024-01"],"award-info":[{"award-number":["Yukeshi2024-01"]}]},{"name":"Graduate Education Reform Project of Henan Province","award":["2025JGLX383Y"],"award-info":[{"award-number":["2025JGLX383Y"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Object detection under adverse weather conditions remains a challenging problem for autonomous driving systems due to image degradation caused by rain, snow, haze, and complex illumination. Although RT-DETR achieves a good balance between detection accuracy and efficiency, its performance is limited in adverse weather scenarios due to weather-induced feature distortion and unreliable query selection. In this paper, we propose WFG-RT-DETR, a robust real-time object detection framework for adverse weather conditions. A Weather-Focused Guidance (WFG) module is introduced to enhance feature representation by combining frequency-domain noise suppression with spatial structural compensation. Furthermore, a Weather-Aware Query Selection (WAQS) module is proposed to improve query initialization by incorporating weather-aware noise estimation, reducing false-positive proposals caused by environmental interference. An Exponential Moving Average (EMA) strategy is also employed to stabilize model training. Extensive experiments on the DAWN and WEDGE datasets demonstrate the effectiveness of the proposed method. Compared with RT-DETR, WFG-RT-DETR improves the mAP@50 on the DAWN dataset from 64.62% to 68.89% while maintaining real-time inference capability. Cross-dataset evaluations on the WEDGE dataset further verify its robustness and generalization under diverse adverse weather conditions. The proposed framework provides an effective solution for reliable object detection in intelligent transportation and autonomous driving applications.<\/jats:p>","DOI":"10.3390\/s26175475","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:56Z","timestamp":1788155756000},"page":"5475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WFG-RT-DETR: A Highly Robust Object Detection Model for Autonomous Vehicles in Adverse Weather Conditions"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiaona","family":"Song","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Runqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Lijun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2020.2984801","article-title":"Object detection under rainy conditions for autonomous vehicles: A review of state-of-the-art and emerging techniques","volume":"38","author":"Hnewa","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1137\/0330046","article-title":"Acceleration of stochastic approximation by averaging","volume":"30","author":"Polyak","year":"1992","journal-title":"SIAM J. Control Optim."},{"key":"ref_4","unstructured":"Kenk, M.A., and Hassaballah, M. (2020). Dawn: Vehicle detection in adverse weather nature dataset. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Marathe, A., Ramanan, D., Walambe, R., and Kotecha, K. (2023). Wedge: A multi-weather autonomous driving dataset built from generative vision-language models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPRW59228.2023.00334"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_8","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_12","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., Sun, L., and Wang, J. (2021). Conditional detr for fast training convergence. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"ref_14","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., Xu, J., and Feng, D. (2017). Aod-net: All-in-one dehazing network. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2017.511"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., and Yang, M.H. (2020). Multi-scale boosted dehazing network with dense feature fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIP.2017.2691802","article-title":"Clearing the skies: A deep network architecture for single-image rain removal","volume":"26","author":"Fu","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1109\/TIP.2018.2806202","article-title":"Desnownet: Context-aware deep network for snow removal","volume":"27","author":"Liu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","unstructured":"Chen, S., Ye, T., Liu, Y., and Chen, E. (2022). SnowFormer: Context interaction transformer with scale-awareness for single image desnowing. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., and Yang, M.H. (2022). Restormer: Efficient transformer for high-resolution image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021). Swinir: Image restoration using swin transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_24","first-page":"11908","article-title":"FFA-Net: Feature fusion attention network for single image dehazing","volume":"34","author":"Qin","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Soatto, S. (2020). Fda: Fourier domain adaptation for semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"ref_26","first-page":"4479","article-title":"Fast fourier convolution","volume":"33","author":"Chi","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","first-page":"980","article-title":"Global filter networks for image classification","volume":"34","author":"Rao","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ju, C., Zhao, Y., Miao, S., Li, D., Chai, R., Xie, Y., and Yan, W. (2025). FDA-DETR: A frequency-aware DETR with dynamic query and adaptive multi-task optimization for oriented small object detection. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0330929"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, K., Zhong, Z., Luo, Z., Tian, H., Wang, K., Jiang, H., Xiang, D., and Tang, W. (2026). FCAT: Frequency-Domain Cross-Attention for All-Weather Multispectral Object Detection in Low-Altitude UAV Security Inspection of Urban and Industrial Areas. Remote Sens., 18.","DOI":"10.3390\/rs18050826"},{"key":"ref_30","first-page":"1401","article-title":"A Frequency-guided Sparse Fusion Algorithm for Multispectral Object Detection in Complex Environments","volume":"52","author":"Xiao","year":"2026","journal-title":"Acta Autom. Sin."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020). Zero-reference deep curve estimation for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-light image enhancement via illumination map estimation","volume":"26","author":"Guo","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018). Domain adaptive faster r-cnn for object detection in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020). Bdd100k: A diverse driving dataset for heterogeneous multitask learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","article-title":"Semantic foggy scene understanding with synthetic data","volume":"126","author":"Sakaridis","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1038\/s41598-025-29575-1","article-title":"AWD-YOLO enhancing autonomous driving perception reliability in adverse weather","volume":"16","author":"Yuan","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_38","first-page":"1792","article-title":"Image-adaptive YOLO for object detection in adverse weather conditions","volume":"36","author":"Liu","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kumar, D., and Muhammad, N. (2023). Object detection in adverse weather for autonomous driving through data merging and YOLOv8. Sensors, 23.","DOI":"10.20944\/preprints202309.0050.v1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, T., Teng, Q., Sun, S., Song, W., Zhang, J., and Li, Y. (2026). CW-DETR: An Efficient Detection Transformer for Traffic Signs in Complex Weather. Sensors, 26.","DOI":"10.3390\/s26010325"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lv, K., Yao, C., Huang, K., Ye, S., and Zhuang, J. (2026). FAD-DETR: A Frequency-Adaptive and Haze-Aware Detection Transformer for Autonomous Driving in Adverse Weather. Appl. Sci., 16.","DOI":"10.3390\/app16062913"},{"key":"ref_42","first-page":"60","article-title":"PGEM-DETR: Physics-guided enhancement mechanism for drone-based object detection in adverse visual environments","volume":"13","author":"Zhang","year":"2026","journal-title":"J. Comput. Des. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wu, H., Xu, G., and Zhou, P. (2025). Weather-Robust Insulator Detection for Smart Power Grids: An Enhanced DETR with Dynamic Attention and Scale-Aware Learning in UAV Inspection. Proceedings of the 2025 4th International Conference on Green Energy and Power Systems (ICGEPS), IEEE.","DOI":"10.1109\/ICGEPS65133.2025.11034628"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, J., Zhang, X., and Song, H. (2026). Robust Object Detection in Adverse Weather Conditions: ECL-YOLOv11 for Automotive Vision Systems. Sensors, 26.","DOI":"10.3390\/s26010304"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"\u00d6zcan, \u0130., Altun, Y., and Parlak, C. (2024). Improving YOLO detection performance of autonomous vehicles in adverse weather conditions using metaheuristic algorithms. Appl. Sci., 14.","DOI":"10.3390\/app14135841"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.patrec.2025.10.018","article-title":"MTW-DETR: A multi-task collaborative optimization model for adverse weather object detection","volume":"199","author":"Peng","year":"2026","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1109\/TPAMI.2023.3335410","article-title":"DN-DETR: Accelerate DETR Training by Introducing Query DeNoising","volume":"46","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","first-page":"10217","article-title":"DINO: DETR with Improved DeNoising Anchor Boxes","volume":"45","author":"Zhang","year":"2023","journal-title":"IEEE TPAMI"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hong, S.J., Choi, C.Y., and Lee, S.W. (2025). Layer-Wise Query Selection to Eliminate Redundant Queries in DETR. Appl. Sci., 15.","DOI":"10.3390\/app15147686"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"106097","DOI":"10.1016\/j.imavis.2026.106097","article-title":"AQS-IDETR: Adaptive Query Selection for Efficient Inference in Real-Time Detection Transformers","volume":"173","author":"Minh","year":"2026","journal-title":"Image Vis. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"23925","DOI":"10.1109\/JSTARS.2025.3607373","article-title":"Compact DETR: A More Compact Real-Time Object Detector for Remote Sensing Object Detection","volume":"18","author":"Chen","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zheng, J., Li, C., Xu, Z., Yang, J., Wei, Q., and Wu, X. (2024). Ccdn-detr: A detection transformer based on constrained contrast denoising for multi-class synthetic aperture radar object detection. Sensors, 24.","DOI":"10.3390\/s24061793"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129818","DOI":"10.1016\/j.neucom.2025.129818","article-title":"Correlation-based switching mean teacher for semi-supervised medical image segmentation","volume":"633","author":"Deng","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"107984","DOI":"10.52202\/079017-3429","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_55","first-page":"78433","article-title":"Yolov12: Attention-centric real-time object detectors","volume":"38","author":"Tian","year":"2026","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wiseman, Y. (2017, January 14\u201317). Real-time monitoring of traffic congestions. Proceedings of the 2017 IEEE International Conference on Electro Information Technology (EIT), Lincoln, NE, USA.","DOI":"10.1109\/EIT.2017.8053413"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5475\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:28:43Z","timestamp":1788236923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5475"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,29]]},"references-count":56,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175475"],"URL":"https:\/\/doi.org\/10.3390\/s26175475","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,29]]}}}