{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:25:40Z","timestamp":1757629540797,"version":"3.44.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045454","type":"print"},{"value":"9783032045461","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04546-1_14","type":"book-chapter","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:54:18Z","timestamp":1757516058000},"page":"160-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PSRDET: Fast Multimodal Detection Based on\u00a0Prior Scene Repair for\u00a0All-Weather Road Sensing"],"prefix":"10.1007","author":[{"given":"Chengbo","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengshi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Chen, Z., Zhong, F., Luo, Q., Zhang, X., Zheng, Y.: EdgeViT: efficient visual modeling for edge computing. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds.) WASA 2022. LNCS, vol. 13473, pp. 393\u2013405. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19211-1_33","DOI":"10.1007\/978-3-031-19211-1_33"},{"issue":"30","key":"14_CR3","doi-asserted-by":"publisher","first-page":"47773","DOI":"10.1007\/s11042-023-15333-w","volume":"82","author":"X Cheng","year":"2023","unstructured":"Cheng, X., Geng, K., Wang, Z., Wang, J., Sun, Y., Ding, P.: SLBAF-Net: super-lightweight bimodal adaptive fusion network for UAV detection in low recognition environment. Multimed. Tools Appl. 82(30), 47773\u201347792 (2023)","journal-title":"Multimed. Tools Appl."},{"key":"14_CR4","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4227745","author":"Q Fang","year":"2022","unstructured":"Fang, Q., Han, D., Wang, Z.: Cross-modality fusion transformer for multispectral object detection. SSRN Electron. J. (2022). https:\/\/doi.org\/10.2139\/ssrn.4227745","journal-title":"SSRN Electron. J."},{"key":"14_CR5","unstructured":"Fang, Y., et al.: You only look at one sequence: rethinking transformer in vision through object detection. In: Neural Information Processing Systems (2021)"},{"key":"14_CR6","doi-asserted-by":"publisher","unstructured":"Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV) (2015). https:\/\/doi.org\/10.1109\/iccv.2015.169","DOI":"10.1109\/iccv.2015.169"},{"key":"14_CR7","doi-asserted-by":"publisher","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (2014). https:\/\/doi.org\/10.1109\/cvpr.2014.81","DOI":"10.1109\/cvpr.2014.81"},{"key":"14_CR8","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces (2023)"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Guo, C., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1780\u20131789 (2020)","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"14_CR10","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO: software for object detection (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Rethinking vision transformers for MobileNet size and speed (2022)","DOI":"10.1109\/ICCV51070.2023.01549"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017). https:\/\/doi.org\/10.1109\/iccv.2017.324,","DOI":"10.1109\/iccv.2017.324"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5802\u20135811 (2022)","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"14_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"14_CR15","unstructured":"Liu, Y., et al.: VMamba: visual state space model (2024)"},{"issue":"7","key":"14_CR16","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., Ma, Y.: SwinFusion: cross-domain long-range learning for general image fusion via swin transformer. IEEE\/CAA J. Autom. Sinica 9(7), 1200\u20131217 (2022)","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"14_CR17","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024)"},{"key":"14_CR18","unstructured":"Mehta, S., Rastegari, M.: MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer (2021)"},{"key":"14_CR19","doi-asserted-by":"publisher","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 1137\u20131149 (2017).https:\/\/doi.org\/10.1109\/tpami.2016.2577031,","DOI":"10.1109\/tpami.2016.2577031"},{"key":"14_CR20","doi-asserted-by":"publisher","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 1137\u20131149 (2017).https:\/\/doi.org\/10.1109\/tpami.2016.2577031","DOI":"10.1109\/tpami.2016.2577031"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Sharma, M., et al.: YOLOrs: object detection in multimodal remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 14, 1497\u20131508 (2020)","DOI":"10.1109\/JSTARS.2020.3041316"},{"key":"14_CR22","doi-asserted-by":"publisher","first-page":"109913","DOI":"10.1016\/j.patcog.2023.109913","volume":"145","author":"J Shen","year":"2024","unstructured":"Shen, J., Chen, Y., Liu, Y., Zuo, X., Fan, H., Yang, W.: Icafusion: iterative cross-attention guided feature fusion for multispectral object detection. Pattern Recogn. 145, 109913 (2024)","journal-title":"Pattern Recogn."},{"key":"14_CR23","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.inffus.2022.10.034","volume":"91","author":"L Tang","year":"2023","unstructured":"Tang, L., Xiang, X., Zhang, H., Gong, M., Ma, J.: DIVFusion: darkness-free infrared and visible image fusion. Inf. Fusion 91, 477\u2013493 (2023)","journal-title":"Inf. Fusion"},{"key":"14_CR24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83","author":"L Tang","year":"2022","unstructured":"Tang, L., Yuan, J., Zhang, H., Jiang, X., Ma, J.: PIAFusion: a progressive infrared and visible image fusion network based on illumination aware. Inf. Fusion 83, 79\u201392 (2022)","journal-title":"Inf. Fusion"},{"key":"14_CR25","unstructured":"Wang, C., et al.: Gold-YOLO: efficient object detector via gather-and-distribute mechanism Sep 2023)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Wang, C., Sun, D., Yang, J., Li, Z., Gao, Q.: DFECF-DET: all-weather detector based on differential feature enhancement and cross-modal fusion with visible and infrared sensors. IEEE Sens. J. (2023)","DOI":"10.1109\/JSEN.2023.3324451"},{"key":"14_CR27","unstructured":"Wang, Z., Li, C., Xu, H., Zhu, X.: Mamba YOLO: SSMS-based yolo for object detection. arXiv preprint arXiv:2406.05835 (2024)"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Meng, F., Wu, Q., Xu, L., He, M., Li, H.: GM-DETR: generalized muiltispectral detection transformer with efficient fusion encoder for visible-infrared detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5541\u20135549 (2024)","DOI":"10.1109\/CVPRW63382.2024.00563"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Yu, W., Wang, X.: MambaOut: do we really need mamba for vision? arXiv preprint arXiv:2405.07992 (2024)","DOI":"10.1109\/CVPR52734.2025.00423"},{"key":"14_CR30","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L., Shum, H.Y.: DINO: DETR with improved denoising anchor boxes for end-to-end object detection (2022)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Fromont, E., Lefevre, S., Avignon, B.: Multispectral fusion for object detection with cyclic fuse-and-refine blocks. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 276\u2013280. IEEE (2020)","DOI":"10.1109\/ICIP40778.2020.9191080"},{"key":"14_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, M., Yu, Y., Jin, S., Gu, L., Ling, T., Tao, X.: VM-UNet-V2: rethinking vision mamba UNet for medical image segmentation. In: Peng, W., Cai, Z., Skums, P. (eds.) ISBRA 2024. LNCS, vol 14954, pp. 335\u2013346. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-981-97-5128-0_27","DOI":"10.1007\/978-981-97-5128-0_27"},{"key":"14_CR33","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Lu, X., Cao, G., Yang, Y., Jiao, L., Liu, F.: ViT-YOLO: transformer-based YOLO for object detection. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW) (2021).https:\/\/doi.org\/10.1109\/iccvw54120.2021.00314","DOI":"10.1109\/iccvw54120.2021.00314"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: DETRs beat YOLOs on real-time object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16965\u201316974 (2024)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"14_CR35","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv Computer Vision and Pattern Recognition (2020)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04546-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:54:26Z","timestamp":1757516066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04546-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"ISBN":["9783032045454","9783032045461"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04546-1_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"11 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}