{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:30:38Z","timestamp":1768811438965,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Science and Technology Foundation","award":["(2016) 1054"],"award-info":[{"award-number":["(2016) 1054"]}]},{"name":"Guizhou Science and Technology Foundation","award":["LH (2017) 7226"],"award-info":[{"award-number":["LH (2017) 7226"]}]},{"name":"Guizhou Science and Technology Foundation","award":["(2017) 5788"],"award-info":[{"award-number":["(2017) 5788"]}]},{"name":"Guizhou Province Joint funding Project","award":["(2016) 1054"],"award-info":[{"award-number":["(2016) 1054"]}]},{"name":"Guizhou Province Joint funding Project","award":["LH (2017) 7226"],"award-info":[{"award-number":["LH (2017) 7226"]}]},{"name":"Guizhou Province Joint funding Project","award":["(2017) 5788"],"award-info":[{"award-number":["(2017) 5788"]}]},{"name":"Guizhou University Academic New Seeding training and Innovation and Exploration Project","award":["(2016) 1054"],"award-info":[{"award-number":["(2016) 1054"]}]},{"name":"Guizhou University Academic New Seeding training and Innovation and Exploration Project","award":["LH (2017) 7226"],"award-info":[{"award-number":["LH (2017) 7226"]}]},{"name":"Guizhou University Academic New Seeding training and Innovation and Exploration Project","award":["(2017) 5788"],"award-info":[{"award-number":["(2017) 5788"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Deep-learning techniques have significantly improved object detection performance, especially with binocular images in 3D scenarios. To supervise the depth information in stereo 3D object detection, reconstructing the 3D dense depth of LiDAR point clouds causes higher computational costs and lower inference speed. After exploring the intrinsic relationship between the implicit depth information and semantic texture features of the binocular images, we propose an efficient and accurate 3D object detection algorithm, FCNet, in stereo images. First, we construct a multi-scale cost\u2013volume containing implicit depth information using the normalized dot-product by generating multi-scale feature maps from the input stereo images. Secondly, the variant attention model enhances its global and local description, and the sparse region monitors the depth loss deep regression. Thirdly, for balancing the channel information preservation of the re-fused left\u2013right feature maps and computational burden, a reweighting strategy is employed to enhance the feature correlation in merging the last-layer features of binocular images. Extensive experiment results on the challenging KITTI benchmark demonstrate that the proposed algorithm achieves better performance, including a lower computational cost and higher inference speed in 3D object detection.<\/jats:p>","DOI":"10.3390\/e24081121","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T01:47:21Z","timestamp":1660528041000},"page":"1121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["FCNet: Stereo 3D Object Detection with Feature Correlation Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3723-3145","authenticated-orcid":false,"given":"Yingyu","family":"Wu","sequence":"first","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9191-2790","authenticated-orcid":false,"given":"Ziyan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"},{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Yunlei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}]},{"given":"Xuhui","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}]},{"given":"Mo","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6610-3166","authenticated-orcid":false,"given":"Guangming","family":"Tang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"ref_1","unstructured":"Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T., Guo, J., Ngiam, J., and Vasudevan, V. (2020, January 16\u201318). End-to-end multi-view fusion for 3d object detection in lidar point clouds. Proceedings of the Conference on Robot Learning, Virtual Event."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, Y., Han, C., Zhang, L., and Gao, X. (2022). Pedestrian detection with multi-view convolution fusion algorithm. Entropy, 24.","DOI":"10.3390\/e24020165"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., and Li, H. (2020, January 14\u201319). Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., and Li, H. (2020). Voxel r-cnn: Towards high performance voxel-based 3d object detection. arXiv.","DOI":"10.1609\/aaai.v35i2.16207"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., and Jia, J. (2020, January 14\u201319). 3dssd: Point-based 3d single stage object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Shen, X., and Jia, J. (2019, January 27\u201328). Std: Sparse-to-dense 3d object detector for point cloud. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00204"},{"key":"ref_7","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, Honolulu, HI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Huang, D., and Wang, Y. (2020). PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection. arXiv.","DOI":"10.1609\/aaai.v35i4.16456"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 18\u201323). Voxelnet: End-to-end learning for point cloud based 3d object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_10","unstructured":"Peng, L., Liu, F., Yan, S., He, X., and Cai, D. (2021). Ocm3d: Object-centric monocular 3d object detection. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., and Urtasun, R. (2016, January 27\u201330). Monocular 3d object detection for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.236"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, P., Zhao, H., Liu, P., and Cao, F. (2020, January 23\u201328). Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58580-8_38"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Luo, S., Dai, H., Shao, L., and Ding, Y. (2021, January 21\u201325). M3DSSD: Monocular 3D single stage object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00608"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1109\/TIP.2019.2952201","article-title":"Monofenet: Monocular 3d object detection with feature enhancement networks","volume":"29","author":"Bao","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, H., Huang, Y., Tian, W., Gao, Z., and Xiong, L. (2021, January 20\u201325). Monorun: Monocular 3d object detection by reconstruction and uncertainty propagation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01024"},{"key":"ref_16","unstructured":"You, Y., Wang, Y., Chao, W.L., Garg, D., Pleiss, G., Hariharan, B., Campbell, M., and Weinberger, K.Q. (2019). Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, P., Chen, X., and Shen, S. (2019, January 15\u201320). Stereo r-cnn based 3d object detection for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00783"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, L., and Liu, M. (June, January 30). Yolostereo3d: A step back to 2d for efficient stereo 3d detection. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561423"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.neucom.2021.11.048","article-title":"Stereo CenterNet-based 3D object detection for autonomous driving","volume":"471","author":"Shi","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., and Weinberger, K.Q. (2019, January 16\u201317). Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00864"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mousavian, A., Anguelov, D., Flynn, J., and Kosecka, J. (2017, January 21\u201326). 3d bounding box estimation using deep learning and geometry. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.597"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/TNNLS.2016.2516565","article-title":"Distributed recurrent neural networks for cooperative control of manipulators: A game-theoretic perspective","volume":"28","author":"Li","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1109\/TNNLS.2016.2574363","article-title":"Kinematic control of redundant manipulators using neural networks","volume":"28","author":"Li","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qian, R., Garg, D., Wang, Y., You, Y., Belongie, S., Hariharan, B., Campbell, M., Weinberger, K.Q., and Chao, W.L. (2020, January 13\u201319). End-to-end pseudo-lidar for image-based 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00592"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sun, J., Chen, L., Xie, Y., Zhang, S., Jiang, Q., Zhou, X., and Bao, H. (2020, January 13\u201319). Disp r-cnn: Stereo 3d object detection via shape prior guided instance disparity estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01056"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pon, A.D., Ku, J., Li, C., and Waslander, S.L. (August, January 31). Object-centric stereo matching for 3d object detection. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196660"},{"key":"ref_27","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, D., Anguelov, D., and Jain, A. (2018, January 18\u201323). Pointfusion: Deep sensor fusion for 3d bounding box estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00033"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huynh, L., Nguyen, P., Matas, J., Rahtu, E., and Heikkil\u00e4, J. (2021, January 11\u201317). Boosting monocular depth estimation with lightweight 3d point fusion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01253"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, W., Schwing, A.G., and Urtasun, R. (2016, January 27\u201330). Efficient deep learning for stereo matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.614"},{"key":"ref_31","first-page":"2287","article-title":"Stereo matching by training a convolutional neural network to compare image patches","volume":"17","author":"Zbontar","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chang, J.R., and Chen, Y.S. (2018, January 18\u201323). Pyramid stereo matching network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qin, Z., Wang, J., and Lu, Y. (2019, January 16\u201317). Triangulation learning network: From monocular to stereo 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00780"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, Z., Wang, Q., Zhang, J., Wei, G., and Chu, X. (2021, January 20\u201325). EDNet: Efficient Disparity Estimation with Cost Volume Combination and Attention-based Spatial Residual. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00539"},{"key":"ref_35","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. arXiv."},{"key":"ref_36","unstructured":"Shi, S., Wang, Z., Wang, X., and Li, H. (2019). Part-A2 net: 3d part-aware and aggregation neural network for object detection from point cloud. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, D., Lu, F., Fang, J., and Zhang, L. (2021, January 11\u201317). Autoshape: Real-time shape-aware monocular 3d object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01535"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, L., Du, L., Ye, X., Fu, Y., Guo, G., Xue, X., Feng, J., and Zhang, L. (2021, January 20\u201325). Depth-conditioned dynamic message propagation for monocular 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00052"},{"key":"ref_39","first-page":"5170","article-title":"Monogrnet: A general framework for monocular 3d object detection","volume":"44","author":"Qin","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, P., Su, S., and Zhao, H. (2021, January 2\u20139). RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i3.16288"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yan, Y., Mao, Y., and Li, B. (2018). Second: Sparsely embedded convolutional detection. Sensors, 18.","DOI":"10.3390\/s18103337"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The kitti vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_46","unstructured":"Liu, C., Gu, S., Van Gool, L., and Timofte, R. (2021, January 22\u201325). Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the 32nd British Machine Vision Conference (BMVC 2021), Online."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Shen, X., and Jia, J. (2020, January 13\u201319). Dsgn: Deep stereo geometry network for 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01255"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lu, J., and Zhou, J. (2021, January 20\u201325). Objects are different: Flexible monocular 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00330"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, X., Xue, N., and Wu, T. (2021). Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. arXiv.","DOI":"10.1609\/aaai.v36i2.20074"},{"key":"ref_50","unstructured":"Weng, X., and Kitani, K. (2019). A baseline for 3d multi-object tracking. arXiv."},{"key":"ref_51","unstructured":"Gao, A., Pang, Y., Nie, J., Cao, J., and Guo, Y. (2021). EGFN: Efficient Geometry Feature Network for Fast Stereo 3D Object Detection. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"K\u00f6nigshof, H., Salscheider, N.O., and Stiller, C. (2019, January 27\u201330). Realtime 3d object detection for automated driving using stereo vision and semantic information. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917330"},{"key":"ref_53","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Beltr\u00e1n, J., Guindel, C., Moreno, F.M., Cruzado, D., Garcia, F., and De La Escalera, A. (2018, January 4\u20137). Birdnet: A 3d object detection framework from lidar information. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569311"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:08:32Z","timestamp":1760141312000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":54,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["e24081121"],"URL":"https:\/\/doi.org\/10.3390\/e24081121","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,14]]}}}