{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T14:09:48Z","timestamp":1783865388330,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T00:00:00Z","timestamp":1677283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["12102136"],"award-info":[{"award-number":["12102136"]}]},{"name":"National Natural Science Foundation","award":["51809096"],"award-info":[{"award-number":["51809096"]}]},{"name":"National Natural Science Foundation","award":["2021A1515012059"],"award-info":[{"award-number":["2021A1515012059"]}]},{"name":"National Natural Science Foundation","award":["202102020619"],"award-info":[{"award-number":["202102020619"]}]},{"name":"National Natural Science Foundation","award":["202102021013"],"award-info":[{"award-number":["202102021013"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["12102136"],"award-info":[{"award-number":["12102136"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["51809096"],"award-info":[{"award-number":["51809096"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021A1515012059"],"award-info":[{"award-number":["2021A1515012059"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202102020619"],"award-info":[{"award-number":["202102020619"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202102021013"],"award-info":[{"award-number":["202102021013"]}]},{"name":"Guangzhou Science and Technology Program","award":["12102136"],"award-info":[{"award-number":["12102136"]}]},{"name":"Guangzhou Science and Technology Program","award":["51809096"],"award-info":[{"award-number":["51809096"]}]},{"name":"Guangzhou Science and Technology Program","award":["2021A1515012059"],"award-info":[{"award-number":["2021A1515012059"]}]},{"name":"Guangzhou Science and Technology Program","award":["202102020619"],"award-info":[{"award-number":["202102020619"]}]},{"name":"Guangzhou Science and Technology Program","award":["202102021013"],"award-info":[{"award-number":["202102021013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Underwater object detection is a key technology in the development of intelligent underwater vehicles. Object detection faces unique challenges in underwater applications: blurry underwater images; small and dense targets; and limited computational capacity available on the deployed platforms. To improve the performance of underwater object detection, we proposed a new object detection approach that combines a new detection neural network called TC-YOLO, an image enhancement technique using an adaptive histogram equalization algorithm, and the optimal transport scheme for label assignment. The proposed TC-YOLO network was developed based on YOLOv5s. Transformer self-attention and coordinate attention were adopted in the backbone and neck of the new network, respectively, to enhance feature extraction for underwater objects. The application of optimal transport label assignment enables a significant reduction in the number of fuzzy boxes and improves the utilization of training data. Our tests using the RUIE2020 dataset and ablation experiments demonstrate that the proposed approach performs better than the original YOLOv5s and other similar networks for underwater object detection tasks; moreover, the size and computational cost of the proposed model remain small for underwater mobile applications.<\/jats:p>","DOI":"10.3390\/s23052567","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:10:46Z","timestamp":1677463846000},"page":"2567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["Underwater Object Detection Using TC-YOLO with Attention Mechanisms"],"prefix":"10.3390","volume":"23","author":[{"given":"Kun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanran","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, K., Cui, W., and Chen, C. (2021). Review of Underwater Sensing Technologies and Applications. Sensors, 11.","DOI":"10.3390\/s21237849"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6235319","DOI":"10.1155\/2021\/6235319","article-title":"Sonar Objective Detection Based on Dilated Separable Densely Connected CNNs and Quantum-Behaved PSO Algorithm","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"32412","DOI":"10.1364\/OE.432756","article-title":"Effective solution for underwater image enhancement","volume":"29","author":"Tao","year":"2021","journal-title":"Opt. Express."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rahman, Z., Jobson, D.J., and Woodell, G.A. (1996, January 19). Multi-scale retinex for color image enhancement. Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland.","DOI":"10.1109\/ICIP.1996.560995"},{"key":"ref_5","unstructured":"He, K., Sun, J., and Tang, X. (2009, January 20\u201325). Single Image Haze Removal Using Dark Channel Prior. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Han, Y., Huang, L., Hong, Z., Cao, S., Zhang, Y., and Wang, J. (2021). Deep Supervised Residual Dense Network for Underwater Image Enhancement. Sensors, 21.","DOI":"10.3390\/s21093289"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6129","DOI":"10.1109\/TNNLS.2021.3072414","article-title":"Lightweight Deep Neural Network for Joint Learning of Underwater Object Detection and Color Conversion","volume":"33","author":"Yeh","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"172848","DOI":"10.1109\/ACCESS.2020.3025617","article-title":"Integrate MSRCR and Mask R-CNN to Recognize Underwater Creatures on Small Sample Datasets","volume":"8","author":"Song","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Katayama, T., Song, T., Shimamoto, T., and Jiang, X. (2019, January 27\u201331). GAN-based Color Correction for Underwater Object Detection. Proceedings of the OCEANS 2019 MTS\/IEEE SEATTLE, Seattle, WA, USA.","DOI":"10.23919\/OCEANS40490.2019.8962561"},{"key":"ref_10","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_11","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_13","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_14","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_15","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sung, M., Yu, S.-C., and Girdhar, Y. (2017, January 19\u201322). Vision based real-time fish detection using convolutional neural network. Proceedings of the OCEANS 2017\u2014Aberdeen, Aberdeen, UK.","DOI":"10.1109\/OCEANSE.2017.8084889"},{"key":"ref_17","unstructured":"Pedersen, M., Haurum, J.B., Gade, R., and Moeslund, T. (2019, January 16\u201320). Detection of Marine Animals in a New Underwater Dataset with Varying Visibility. Proceedings of the IEEE Conference on Computer Vision and Pattern recognition Workshops, Long Beach, CA, USA."},{"key":"ref_18","first-page":"1","article-title":"Underwater target detection system based on YOLO v4","volume":"107","author":"Wang","year":"2021","journal-title":"Int. Conf. Artif. Intell. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1109\/TIP.2021.3074738","article-title":"Composited FishNet: Fish Detection and Species Recognition From Low-Quality Underwater Videos","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"33747","DOI":"10.1007\/s11042-021-11230-2","article-title":"Underwater target detection with an attention mechanism and improved scale","volume":"80","author":"Wei","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, L., Ye, X., Xing, H., Wang, Z., and Li, P. (2020, January 5\u201330). YOLO Nano Underwater: A Fast and Compact Object Detector for Embedded Device. Proceedings of the Global Oceans 2020: Singapore\u2014U.S. Gulf Coast, Biloxi, MS, USA.","DOI":"10.1109\/IEEECONF38699.2020.9389213"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101847","DOI":"10.1016\/j.ecoinf.2022.101847","article-title":"YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment","volume":"72","author":"Hasan","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zheng, J., Sun, S., and Zhang, L. (2022). An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection. Symmetry, 14.","DOI":"10.2139\/ssrn.4079287"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106135","DOI":"10.1016\/j.compag.2021.106135","article-title":"Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network","volume":"185","author":"Hu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.-Y., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., and Hsieh, J.-W. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J., Zou, Y., Zhou, D., and Feng, J. (November, January 27). PANet: Few-shot image semantic segmentation with prototype alignment. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00929"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient non-maximum suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Zheng, Y., Yao, L., Qi, S., Tang, L., Yi, H., and Dong, K. (2022, January 24\u201326). Underwater Object Detection with Swin Transformer. Proceedings of the 2022 4th International Conference on Data Intelligence and Security (ICDIS), Shenzhen, China.","DOI":"10.1109\/ICDIS55630.2022.00070"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, J., Zhu, Y., Chen, M., Wang, Y., and Zhou, Z. (2022, January 21\u201324). Research on Underwater Small Target Detection Algorithm Based on Improved YOLOv3. Proceedings of the 2022 16th IEEE International Conference on Signal Processing (ICSP), Beijing, China.","DOI":"10.1109\/ICSP56322.2022.9965317"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhai, X., Wei, H., He, Y., Shang, Y., and Liu, C. (2022). Underwater Sea Cucumber Identification Based on Improved YOLOv5. Appl. Sci., 12.","DOI":"10.3390\/app12189105"},{"key":"ref_33","first-page":"1","article-title":"Spatial transformer networks","volume":"28","author":"Jaderberg","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","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 Computer Vision\u2014ECCV 2018: 15th European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_37","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_38","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 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.neucom.2021.07.094","article-title":"LLA: Loss-aware label assignment for dense pedestrian detection","volume":"462","author":"Ge","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xu, C., Wang, J., Yang, W., Yu, H., Yu, L., and Xia, G.-S. (2022, January 18\u201324). RFLA: Gaussian receptive field based label assignment for tiny object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1007\/978-3-031-20077-9_31"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ge, Z., Liu, S., Li, Z., Yoshie, O., and Sun, J. (2021, January 20\u201325). OTA: Optimal transport assignment for object detection. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00037"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6216","DOI":"10.1364\/OE.449930","article-title":"Underwater image enhancement using adaptive color restoration and dehazing","volume":"30","author":"Li","year":"2022","journal-title":"Opt. Express"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5664","DOI":"10.1109\/TIP.2016.2612882","article-title":"Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6707328","DOI":"10.1155\/2020\/6707328","article-title":"Underwater image processing and object detection based on deep CNN method","volume":"2020","author":"Han","year":"2020","journal-title":"J. Sens."},{"key":"ref_46","first-page":"160","article-title":"A survey on underwater image enhancement techniques","volume":"87","author":"Sahu","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"012026","DOI":"10.1088\/1742-6596\/1019\/1\/012026","article-title":"A review of histogram equalization techniques in image enhancement application","volume":"1019","author":"Mustafa","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive histogram equalization and its variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1023\/B:VLSI.0000028532.53893.82","article-title":"Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement","volume":"38","author":"Reza","year":"2014","journal-title":"J. Vlsi Signal Process. Syst. Signal Image Video Technol."},{"key":"ref_50","unstructured":"Rahman, Z., Woodell, G.A., and Jobson, D.J. (1997). A Comparison of the Multiscale Retinex with Other Image Enhancement Techniques, NASA. NASA Technical Report 20040110657."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4861","DOI":"10.1109\/TCSVT.2019.2963772","article-title":"Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light","volume":"30","author":"Liu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_53","unstructured":"Goyal, P., Doll\u00e1r, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K. (2017). Accurate, large minibatch SGD: Training imagenet in 1 hour. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Khasawneh, N., Fraiwan, M., and Fraiwan, L. (2022). Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3. Clust. Comput., 1\u201311.","DOI":"10.1007\/s10586-022-03802-0"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_56","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO series in 2021. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_58","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 (ICML 2019), Long Beach, CA, USA."},{"key":"ref_59","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the NIPS\u201915: Proceedings of the 28th International Conference on Neural Information Processing Systems, Cambridge, MA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2567\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:42:31Z","timestamp":1760121751000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,25]]},"references-count":59,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052567"],"URL":"https:\/\/doi.org\/10.3390\/s23052567","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,25]]}}}