{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:01:14Z","timestamp":1775066474429,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MSIT (Ministry of Science and ICT), Korea","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection is one of the vital components used for autonomous navigation in dynamic environments. Camera and lidar sensors have been widely used for efficient object detection by mobile robots. However, they suffer from adverse weather conditions in operating environments such as sun, fog, snow, and extreme illumination changes from day to night. The sensor fusion of camera and lidar data helps to enhance the overall performance of an object detection network. However, the diverse distribution of training data makes the efficient learning of the network a challenging task. To address this challenge, we systematically study the existing visual and lidar features based on object detection methods and propose an adaptive feature attention module (AFAM) for robust multisensory data fusion-based object detection in outdoor dynamic environments. Given the camera and lidar features extracted from the intermediate layers of EfficientNet backbones, the AFAM computes the uncertainty among the two modalities and adaptively refines visual and lidar features via attention along the channel and the spatial axis. The AFAM integrated with the EfficientDet performs the adaptive recalibration and fusion of visual lidar features by filtering noise and extracting discriminative features for an object detection network under specific environmental conditions. We evaluate the AFAM on a benchmark dataset exhibiting weather and light variations. The experimental results demonstrate that the AFAM significantly enhances the overall detection accuracy of an object detection network.<\/jats:p>","DOI":"10.3390\/rs15163992","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T10:33:23Z","timestamp":1691750003000},"page":"3992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Adaptive Feature Attention Module for Robust Visual\u2013LiDAR Fusion-Based Object Detection in Adverse Weather Conditions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8001-4212","authenticated-orcid":false,"given":"Taek-Lim","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Control and Robot Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea"}]},{"given":"Saba","family":"Arshad","sequence":"additional","affiliation":[{"name":"Industrial Artificial Intelligence Research Center, Chungbuk National University, Cheongju 28644, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3695-344X","authenticated-orcid":false,"given":"Tae-Hyoung","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems & Robotics, Chungbuk National University, Cheongju 28644, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khairuddin, A.R., Talib, M.S., and Haron, H. (2016, January 27\u201329). Review on simultaneous localization and mapping (SLAM). Proceedings of the 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015, Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2015.7482163"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3782","DOI":"10.1109\/TITS.2019.2892405","article-title":"A Survey on 3D Object Detection Methods for Autonomous Driving Applications","volume":"20","author":"Arnold","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38297","DOI":"10.1007\/s11042-022-13153-y","article-title":"Tools, techniques, datasets and application areas for object detection in an image: A review","volume":"81","author":"Kaur","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liang, M., Yang, B., Chen, Y., Hu, R., and Urtasun, R. (2019, January 15\u201320). Multi-Task Multi-Sensor Fusion for 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00752"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ku, J., Mozifian, M., Lee, J., Harakeh, A., and Waslander, S.L. (2018, January 1\u20135). Joint 3D Proposal Generation and Object Detection from View Aggregation. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594049"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vora, S., Lang, A.H., Helou, B., and Beijbom, O. (2020, January 13\u201319). PointPainting: Sequential Fusion for 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). Multi-View 3D Object Detection Network for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_8","unstructured":"Huang, T., Liu, Z., Chen, X., and Bai, X. (2020). Computer Vision\u2013ECCV 2020, Springer Science and Business Media Deutschland GmbH."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Y., Yu, A.W., Meng, T., Caine, B., Ngiam, J., Peng, D., Shen, J., Lu, Y., Zhou, D., and Le Quoc, V. (2022, January 18\u201324). DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01667"},{"key":"ref_10","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is All you Need. Adv. Neural Inf. Process Syst., 30."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, D., Anguelov, D., and Jain, A. (2018, January 1\u20135). PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Madrid, Spain.","DOI":"10.1109\/CVPR.2018.00033"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., and Heide, F. (2020, January 13\u201319). Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, T.L., and Park, T.H. (2022). Camera-LiDAR Fusion Method with Feature Switch Layer for Object Detection Networks. Sensors, 22.","DOI":"10.3390\/s22197163"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_15","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: Challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimed Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 1\u20135). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Madrid, Spain.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_20","first-page":"975","article-title":"A Review on Methods of Image Dehazing","volume":"133","author":"Patel","year":"2016","journal-title":"Int. J. Comput. Appl."},{"key":"ref_21","unstructured":"Zhang, Z., Zhao, L., Liu, Y., Zhang, S., and Yang, J. (December, January 30). Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes. Proceedings of the Asian Conference on Computer Vision (ACCV), Kyoto, Janpan."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, W.-T., Ding, J.-J., and Kuo, S.-Y. (2019, January 15\u201320). PMS-Net: Robust Haze Removal Based on Patch Map for Single Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01195"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Berman, D., Treibitz, T., and Avidan, S. (2016, January 27\u201330). Non-Local Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.185"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., Xu, J., and Feng, D. (2017, January 22\u201329). AOD-Net: All-In-One Dehazing Network. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.511"},{"key":"ref_25","unstructured":"Liu, X., Ma, Y., Shi, Z., and Chen, J. (November, January 27). GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_26","unstructured":"Zeng, C., and Kwong, S. (2022). Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4486","DOI":"10.1109\/TCSVT.2021.3127149","article-title":"SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection","volume":"32","author":"Liu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_29","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_30","first-page":"1527","article-title":"Weather influence and classification with automotive lidar sensors","volume":"2019","author":"Heinzler","year":"2019","journal-title":"IEEE Intell. Veh. Symp. Proc."},{"key":"ref_31","first-page":"777","article-title":"RangeWeatherNet for LiDAR-only weather and road condition classification","volume":"2021","author":"Sebastian","year":"2021","journal-title":"IEEE Intell. Veh. Symp. Proc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/LRA.2020.2972865","article-title":"CNN-Based Lidar Point Cloud De-Noising in Adverse Weather","volume":"5","author":"Heinzler","year":"2020","journal-title":"IEEE Robot Autom. Lett."},{"key":"ref_33","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2019, January 15\u201320). PointPillars: Fast Encoders for Object Detection from Point Clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 1\u20135). VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Madrid, Spain.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_36","first-page":"1201","article-title":"Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection","volume":"35","author":"Deng","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mao, J., Xue, Y., Niu, M., Bai, H., Feng, J., Liang, X., Xu, H., and Xu, C. (2021, January 10\u201317). Voxel Transformer for 3D Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00315"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Meyer, G.P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., and Wellington, C.K. (2019, January 15\u201320). LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01296"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pang, S., Morris, D., and Radha, H. (2020). CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. IEEE Int. Conf. Intell. Robot. Syst., 10386\u201310393.","DOI":"10.1109\/IROS45743.2020.9341791"},{"key":"ref_40","unstructured":"Cai, Z., Fan, Q., Feris, R.S., and Vasconcelos, N. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hoffman, J., Gupta, S., and Darrell, T. (2016, January 27\u201330). Learning with Side Information Through Modality Hallucination. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.96"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Song, S., and Xiao, J. (2016, January 27\u201330). Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.94"},{"key":"ref_43","unstructured":"Gsai, S.L., Suha, T.A., and Gsai, K. (2022, January 18\u201324). FIFO: Learning Fog-Invariant Features for Foggy Scene Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., and Lin, D. (2018, January 1\u20135). Unsupervised Feature Learning via Non-Parametric Instance Discrimination. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Madrid, Spain.","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref_45","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Com, W., and Deepmind, G. (2015, January 6\u201311). Weight Uncertainty in Neural Network. Proceedings of the 32nd International Conference on Machine Learning, PMLR, Lile, France."},{"key":"ref_46","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_47","unstructured":"(2023, May 01). GitHub\u2013lukemelas\/EfficientNet-PyTorch: A PyTorch Implementation of EfficientNet and EfficientNetV2. Available online: https:\/\/github.com\/lukemelas\/EfficientNet-PyTorch."},{"key":"ref_48","first-page":"15475","article-title":"ResT: An Efficient Transformer for Visual Recognition","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3992\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:31:32Z","timestamp":1760128292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,11]]},"references-count":48,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15163992"],"URL":"https:\/\/doi.org\/10.3390\/rs15163992","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,11]]}}}