{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:06:08Z","timestamp":1785420368329,"version":"3.56.0"},"reference-count":42,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"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":"publisher","award":["62406207"],"award-info":[{"award-number":["62406207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62406207"],"award-info":[{"award-number":["62406207"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Open Research Fund of the National Key Laboratory","award":["KFJJ202403"],"award-info":[{"award-number":["KFJJ202403"]}]},{"name":"Scientific Research Start-up Fund for High-Level Talents","award":["XYKY2025055"],"award-info":[{"award-number":["XYKY2025055"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address insufficient detail representation, inadequate cross-modal fusion, and limited localization accuracy in object detection under low-light and complex background conditions, this study proposes an improved CenterNet-based multimodal object detection method. The model uses fused images and infrared images as dual-source inputs, where infrared wavelet priors are introduced to enhance texture and structural representation. A Feature Fusion Attention (FFA) module is designed to improve cross-modal feature interaction, while a Heatmap-Guided Detection Head (HGDH) is introduced to explicitly enhance target-related regions during detection. In addition, a two-stack Hourglass backbone is adopted for multi-scale modeling of global semantics and local details. Based on the public LLVIP dataset, a precisely paired fused-image\/infrared-image dataset, termed RH-25, was constructed for experiments. On the RH-25 dataset, the proposed method achieves a 3.51% improvement in mAP@0.5 relative to the baseline, as demonstrated by comparative and ablation experiments. Moreover, supplementary experiments on the MFAD dataset indicate potential cross-dataset adaptability under different scenes and multi-class conditions. These results indicate that the proposed method can improve detection performance in low-light and complex environments.<\/jats:p>","DOI":"10.3390\/s26123735","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T01:52:08Z","timestamp":1781229128000},"page":"3735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved CenterNet-Based Multimodal Object Detection for Low-Light and Complex Environments"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5072-4461","authenticated-orcid":false,"given":"Zhigang","family":"Yao","sequence":"first","affiliation":[{"name":"College of Aviation and Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengxin","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Aviation and Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huazhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Aviation and Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China"},{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Aviation and Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juhang","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Aviation and Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Samu, J., and Yang, C. (2025). Airport ground-based aerial object surveillance technologies for enhanced safety: A systematic review. Drones, 10.","DOI":"10.3390\/drones10010022"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100804","DOI":"10.1016\/j.cosrev.2025.100804","article-title":"Low-light multimodal object detection: A survey","volume":"58","author":"Li","year":"2025","journal-title":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., and Zhou, W. (2021). LLVIP: A visible-infrared paired dataset for low-light vision. Proceedings of the 18th IEEE\/CVF International Conference on Computer Vision (ICCV), Electr Network, IEEE.","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, J., Fan, X., Huang, Z., Wu, G., Liu, R., Zhong, W., and Luo, Z. (2022). Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105107","DOI":"10.1016\/j.infrared.2023.105107","article-title":"An object detection algorithm based on infrared-visible dual modal feature fusion","volume":"137","author":"Hou","year":"2024","journal-title":"Infrared Phys. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xiao, X., Wang, B., Miao, L., Li, L., Zhou, Z., Ma, J., and Dong, D. (2021). Infrared and visible image object detection via focused feature enhancement and cascaded semantic extension. Remote Sens., 13.","DOI":"10.3390\/rs13132538"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hu, Z., Jing, Y., and Wu, G. (2023). Decision-level fusion detection method of visible and infrared images under low light conditions. EURASIP J. Adv. Signal Process., 2023.","DOI":"10.1186\/s13634-023-01002-5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2022.06.008","article-title":"Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection","volume":"88","author":"Cao","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102352","DOI":"10.1016\/j.inffus.2024.102352","article-title":"A semantic-driven coupled network for infrared and visible image fusion","volume":"108","author":"Liu","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1049\/ipr2.12616","article-title":"I-CenterNet: Road infrared target detection based on improved CenterNet","volume":"17","author":"Li","year":"2023","journal-title":"IET Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12991","DOI":"10.1038\/s41598-026-35339-2","article-title":"A study on infrared-visible fusion multimodal object detection algorithm based on cross-modal information bottleneck and minimum redundancy transformation","volume":"16","author":"Tan","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shi, T., Gong, J., Hu, J., Zhi, X., Zhang, W., Zhang, Y., Zhang, P., and Bao, G. (2022). Feature-enhanced CenterNet for small object detection in remote sensing images. Remote Sens., 14.","DOI":"10.3390\/rs14215488"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s10462-025-11426-0","article-title":"Advances and challenges in infrared-visible image fusion: A comprehensive review of techniques and applications","volume":"59","author":"Wang","year":"2026","journal-title":"Artif. Intell. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1109\/TPAMI.2024.3521416","article-title":"Infrared and visible image fusion: From data compatibility to task adaption","volume":"47","author":"Liu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_16","unstructured":"Yang, L., Zhang, R.-Y., Li, L., and Xie, X. (2021). Simam: A simple, parameter-free attention module for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Curran Associates, Inc.. PMLR 2021."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016). Stacked hourglass networks for human pose estimation. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, C., Chen, Y., Qiu, X., Li, R., and You, L. (2024). MRD-YOLO: A multispectral object detection algorithm for complex road scenes. Sensors, 24.","DOI":"10.3390\/s24103222"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thaker, K., Chennupati, S., Rawashdeh, N., and Rawashdeh, S.A. (2023). Multispectral deep neural network fusion method for low-light object detection. J. Imaging, 10.","DOI":"10.3390\/jimaging10010012"},{"key":"ref_20","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_21","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ren, W., Luo, L., and Ren, J. (2025). A two-stage approach for infrared and visible image fusion and segmentation. Appl. Sci., 15.","DOI":"10.3390\/app151910698"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110133","DOI":"10.1016\/j.compeleceng.2025.110133","article-title":"Cross-modality target detection using infrared and visible image fusion for robust objection recognition","volume":"123","author":"Yu","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106346","DOI":"10.1016\/j.infrared.2025.106346","article-title":"RT-DETR: A robust visible-infrared object detector with adaptive cross-modal feature fusion","volume":"153","author":"An","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"ref_25","first-page":"3","article-title":"Infrared small target detection algorithm based on ISTD-CenterNet","volume":"77","author":"Li","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"12833","DOI":"10.3934\/mbe.2022599","article-title":"Weakly perceived object detection based on an improved CenterNet","volume":"19","author":"Zhou","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, D., Wang, Y., Wang, H., Wang, F., and Gao, G. (2024). DCFNet: Infrared and visible image fusion network based on discrete wavelet transform and convolutional neural network. Sensors, 24.","DOI":"10.3390\/s24134065"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, J., Wei, M., Wang, J., Zhu, M., Lin, H., Nie, H., and Deng, X. (2024). CenterADNet: Infrared video target detection based on central point regression. Sensors, 24.","DOI":"10.3390\/s24061778"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"131024","DOI":"10.1016\/j.optcom.2024.131024","article-title":"WaveFusionNet: Infrared and visible image fusion based on multi-scale feature encoder\u2013decoder and discrete wavelet decomposition","volume":"573","author":"Liu","year":"2024","journal-title":"Opt. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110445","DOI":"10.1016\/j.patcog.2024.110445","article-title":"MFIFusion: An infrared and visible image enhanced fusion network based on multi-level feature injection","volume":"152","author":"Dong","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109553","DOI":"10.1016\/j.patcog.2023.109553","article-title":"Keep an eye on faces: Robust face detection with heatmap-assisted spatial attention and scale-aware layer attention","volume":"140","author":"Ju","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","article-title":"Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network","volume":"82","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e14558","DOI":"10.1016\/j.heliyon.2023.e14558","article-title":"A survey on image enhancement for low-light images","volume":"9","author":"Guo","year":"2023","journal-title":"Heliyon"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.isprsjprs.2022.02.012","article-title":"Modelling people\u2019s perceived scene complexity of real-world environments using street-view panoramas and open geodata","volume":"186","author":"Guan","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_38","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_39","unstructured":"Hwang, S., Han, D., and Jeon, M. (2025). Multispectral detection transformer with infrared-centric feature fusion. arXiv."},{"key":"ref_40","unstructured":"Yaseen, M. (2024). What is YOLOv8: An in-depth exploration of the internal features of the next-generation object detector. arXiv."},{"key":"ref_41","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_42","unstructured":"Wu, W., Zhang, X., Yin, H., Dai, S., Zhang, H., and Zhang, Y. (2025). FreDFT: Frequency Domain Fusion Transformer for Visible-Infrared Object Detection. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3735\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T01:54:29Z","timestamp":1781229269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3735"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":42,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123735"],"URL":"https:\/\/doi.org\/10.3390\/s26123735","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]}}}