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for Young Backbone Teachers in Henan University of Technology","award":["242102311015"],"award-info":[{"award-number":["242102311015"]}]},{"name":"Cultivation Programme for Young Backbone Teachers in Henan University of Technology","award":["KFJJ2022013"],"award-info":[{"award-number":["KFJJ2022013"]}]},{"name":"Cultivation Programme for Young Backbone Teachers in Henan University of Technology","award":["2022ZKCJ11"],"award-info":[{"award-number":["2022ZKCJ11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection in remote sensing images has received significant attention for a wide range of applications. However, traditional unimodal remote sensing images, whether based on visible light or infrared, have limitations that cannot be ignored. Visible light images are susceptible to ambient lighting conditions, and their detection accuracy can be greatly reduced. Infrared images often lack rich texture information, resulting in a high false-detection rate during target identification and classification. To address these challenges, we propose a novel multimodal fusion network detection model, named ACDF-YOLO, basedon the lightweight and efficient YOLOv5 structure, which aims to amalgamate synergistic data from both visible and infrared imagery, thereby enhancing the efficiency of target identification in remote sensing imagery. Firstly, a novel efficient shuffle attention module is designed to assist in extracting the features of various modalities. Secondly, deeper multimodal information fusion is achieved by introducing a new cross-modal difference module to fuse the features that have been acquired. Finally, we combine the two modules mentioned above in an effective manner to achieve ACDF. The ACDF not only enhances the characterization ability for the fused features but also further refines the capture and reinforcement of important channel features. Experimental validation was performed using several publicly available multimodal real-world and remote sensing datasets. Compared with other advanced unimodal and multimodal methods, ACDF-YOLO separately achieved a 95.87% and 78.10% mAP0.5 on the LLVIP and VEDAI datasets, demonstrating that the deep fusion of different modal information can effectively improve the accuracy of object detection.<\/jats:p>","DOI":"10.3390\/rs16183532","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T03:49:46Z","timestamp":1727149786000},"page":"3532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["ACDF-YOLO: Attentive and Cross-Differential Fusion Network for Multimodal Remote Sensing Object Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6637-9469","authenticated-orcid":false,"given":"Xuan","family":"Fei","sequence":"first","affiliation":[{"name":"Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou 450001, China"},{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3430-2165","authenticated-orcid":false,"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renping","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_2","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M., and Huang, W. (2021, January 17). TOOD: Task-aligned One-stage Object Detection. Proceedings of the International Conference on Computer Vision 2021, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_6","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5877","DOI":"10.1109\/TIP.2023.3326687","article-title":"Bayesian Nonlocal Patch Tensor Factorization for Hyperspectral Image Super-Resolution","volume":"32","author":"Ye","year":"2023","journal-title":"IEEE Trans. Image Process"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5514116","DOI":"10.1109\/TGRS.2024.3385448","article-title":"Connecting Low-Level and High-Level Visions: A Joint Optimization for Hyperspectral Image Super-Resolution and Target Detection","volume":"62","author":"He","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9805389","DOI":"10.34133\/2021\/9805389","article-title":"Feature Enhancement Network for Object Detection in Optical Remote Sensing Images","volume":"2021","author":"Cheng","year":"2021","journal-title":"J. Remote. Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M., Yang, J., and Li, X. (2023, January 2\u20133). Large Selective Kernel Network for Remote Sensing Object Detection. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fei, X., Wu, S., Miao, J., Wang, G., and Sun, L. (2024). Lightweight-VGG: A Fast Deep Learning Architecture Based on Dimensionality Reduction and Nonlinear Enhancement for Hyperspectral Image Classification. Remote Sens., 16.","DOI":"10.3390\/rs16020259"},{"key":"ref_12","first-page":"5516415","article-title":"MASSFormer: Memory-Augmented Spectral-Spatial Transformer for Hyperspectral Image Classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3588","DOI":"10.1109\/TCYB.2020.3026673","article-title":"Scheduling-Guided Automatic Processing of Massive Hyperspectral Image Classification on Cloud Computing Architectures","volume":"51","author":"Wu","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TIP.2022.3228497","article-title":"UIU-Net: U-Net in U-Net for Infrared Small Object Detection","volume":"32","author":"Wu","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"141861","DOI":"10.1109\/ACCESS.2021.3120870","article-title":"YOLO-FIRI: Improved YOLOv5 for Infrared Image Object Detection","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, B.R., Huo, L.L., and Fan, Y.S. (2024). GT-YOLO: Nearshore Infrared Ship Detection Based on Infrared Images. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12020213"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4611","DOI":"10.1109\/JSTARS.2024.3357171","article-title":"MGSFA-Net: Multiscale Global Scattering Feature Association Network for SAR Ship Target Recognition","volume":"17","author":"Zhang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, X.F., Xia, Y.T., Zhang, W.W., Zheng, C., and Zhang, Z.L. (2023). YOLO-ViT-Based Method for Unmanned Aerial Vehicle Infrared Vehicle Target Detection. Remote Sens., 15.","DOI":"10.3390\/rs15153778"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1109\/JSTARS.2020.3041316","article-title":"YOLOrs: Object Detection in Multimodal Remote Sensing Imagery","volume":"14","author":"Sharma","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_21","first-page":"5605415","article-title":"SuperYOLO: Super resolution assisted object detection in multimodal remote sensing imagery","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, J., Yu, M., Li, S., Zhang, J., and Hu, S. (2023). Long-Tailed Object Detection for Multimodal Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15184539"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47773","DOI":"10.1007\/s11042-023-15333-w","article-title":"SLBAF-Net: Super-Lightweight bimodal adaptive fusion network for UAV detection in low recognition environment","volume":"82","author":"Cheng","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","article-title":"PIAFusion: A progressive infrared and visible image fusion network based on illumination aware","volume":"83","author":"Tang","year":"2022","journal-title":"Inform. Fusion"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fang, Q., Han, D., and Wang, Z. (2022). Cross-Modality Fusion Transformer for Multispectral Object Detection. Ssrn Electron. J.","DOI":"10.2139\/ssrn.4227745"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5403712","DOI":"10.1109\/TGRS.2024.3376819","article-title":"C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection","volume":"62","author":"Yuan","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102147","DOI":"10.1016\/j.inffus.2023.102147","article-title":"CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach","volume":"103","author":"Li","year":"2024","journal-title":"Inform. Fusion"},{"key":"ref_28","first-page":"2100116","article-title":"Multiscale 3-D\u20132-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, W.Y., Miao, L.J., Wang, Y.H., Zhou, Z.Q., and Qiao, Y.J. (2024). Infrared-Visible Image Fusion through Feature-Based Decomposition and Domain Normalization. Remote Sens., 16.","DOI":"10.3390\/rs16060969"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107905","DOI":"10.1016\/j.engappai.2024.107905","article-title":"ASFusion: Adaptive visual enhancement and structural patch decomposition for infrared and visible image fusion","volume":"132","author":"Zhou","year":"2024","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeng, X., and Long, L. (2022). Generative Adversarial Networks. Beginning Deep Learning with TensorFlow, Apress.","DOI":"10.1007\/978-1-4842-7915-1_13"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inform. Fusion"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, C., Yang, P., Zhou, F., Yue, G., Wang, S., Wu, H., Lei, B., Wang, T., and Chen, C. (IEEE Trans. Neural Netw. Learn. Syst., 2023). MHW-GAN: Multidiscriminator Hierarchical Wavelet Generative Adversarial Network for Multimodal Image Fusion, IEEE Trans. Neural Netw. Learn. Syst., early access.","DOI":"10.1109\/TNNLS.2023.3271059"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, H., Liang, P., Yu, W., Jiang, J., and Ma, J. (2019, January 10\u201316). Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19, Macao, China.","DOI":"10.24963\/ijcai.2019\/549"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_38","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2017, January 21\u201325). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Communications of the ACM 2017, Los Angeles, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018, January 8\u201314). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the Computer Vision\u2013ECCV 2018, Lecture Notes in Computer Science, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/LGRS.2016.2637439","article-title":"SeNet: Structured Edge Network for Sea-Land Segmentation","volume":"14","author":"Cheng","year":"2017","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, H., Liu, Y., Zhang, H., and Zheng, D. (2024). Tree-Level Chinese Fir Detection Using UAV RGB Imagery and YOLO-DCAM. Remote Sens., 16.","DOI":"10.3390\/rs16020335"},{"key":"ref_43","first-page":"102912","article-title":"Object detection from UAV thermal infrared images and videos using YOLO models","volume":"112","author":"Jiang","year":"2022","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., and Zhou, W. (2021, January 11\u201317). LLVIP: A Visible-infrared Paired Dataset for Low-light Vision. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A., and Hou, Q. (2021, January 5\u20139). Rotate to Attend: Convolutional Triplet Attention Module. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual Event.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ruan, D., Wang, D., Zheng, Y., Zheng, N., and Zheng, M. (2021, January 19\u201325). Gaussian Context Transformer. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01488"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3532\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:02:51Z","timestamp":1760112171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":46,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183532"],"URL":"https:\/\/doi.org\/10.3390\/rs16183532","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,23]]}}}