{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T18:02:20Z","timestamp":1777917740497,"version":"3.51.4"},"reference-count":59,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["62001418"],"award-info":[{"award-number":["62001418"]}]},{"name":"National Science Foundation of China","award":["62005245"],"award-info":[{"award-number":["62005245"]}]},{"name":"National Science Foundation of China","award":["62036009"],"award-info":[{"award-number":["62036009"]}]},{"name":"National Science Foundation of China","award":["LQ21F010011"],"award-info":[{"award-number":["LQ21F010011"]}]},{"name":"National Science Foundation of China","award":["U1909203"],"award-info":[{"award-number":["U1909203"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62001418"],"award-info":[{"award-number":["62001418"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62005245"],"award-info":[{"award-number":["62005245"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62036009"],"award-info":[{"award-number":["62036009"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ21F010011"],"award-info":[{"award-number":["LQ21F010011"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["U1909203"],"award-info":[{"award-number":["U1909203"]}]},{"name":"Joint Funds of the National Science Foundation of China","award":["62001418"],"award-info":[{"award-number":["62001418"]}]},{"name":"Joint Funds of the National Science Foundation of China","award":["62005245"],"award-info":[{"award-number":["62005245"]}]},{"name":"Joint Funds of the National Science Foundation of China","award":["62036009"],"award-info":[{"award-number":["62036009"]}]},{"name":"Joint Funds of the National Science Foundation of China","award":["LQ21F010011"],"award-info":[{"award-number":["LQ21F010011"]}]},{"name":"Joint Funds of the National Science Foundation of China","award":["U1909203"],"award-info":[{"award-number":["U1909203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model\u2019s ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.<\/jats:p>","DOI":"10.3390\/s22218468","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"8468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images"],"prefix":"10.3390","volume":"22","author":[{"given":"Dongdong","family":"Zhao","sequence":"first","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihao","family":"Ge","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingtian","family":"Hu","sequence":"additional","affiliation":[{"name":"The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjie","family":"Dang","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronghua","family":"Liang","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Guo","sequence":"additional","affiliation":[{"name":"The Institute of Deep-Sea Science and Engineering Chinese Academy of Sciences, Sanya 572000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qin, R., Zhao, X., Zhu, W., Yang, Q., He, B., Li, G., and Yan, T. (2021). Multiple Receptive Field Network (MRF-Net) for Autonomous Underwater Vehicle Fishing Net Detection Using Forward-Looking Sonar Images. Sensors, 21.","DOI":"10.3390\/s21061933"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5336","DOI":"10.1109\/TIP.2019.2910666","article-title":"Reference-Free Quality Assessment of Sonar Images via Contour Degradation Measurement","volume":"28","author":"Chen","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, Y., Li, W., and Yuan, F. (2020). Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8100761"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/JOE.2010.2054175","article-title":"An Efficient Digital CZT Beamforming Design for Near-Field 3-D Sonar Imaging","volume":"35","author":"Palmese","year":"2010","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, R., Li, T., Memon, I., Shi, Y., Ullah, I., and Memon, S.A. (2022). Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment. Sensors, 22.","DOI":"10.3390\/s22093335"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/JOE.2014.2377454","article-title":"Low-Cost Acoustic Cameras for Underwater Wideband Passive Imaging","volume":"40","author":"Trucco","year":"2015","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/JOE.2018.2875574","article-title":"Application of Forward-Scan Sonar Stereo for 3-D Scene Reconstruction","volume":"45","author":"Negahdaripour","year":"2020","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rixon Fuchs, L., Maki, A., and G\u00e4llstr\u00f6m, A. (2022). Optimization Method for Wide Beam Sonar Transmit Beamforming. Sensors, 22.","DOI":"10.3390\/s22197526"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/JOE.2018.2863961","article-title":"Unsupervised Local Spatial Mixture Segmentation of Underwater Objects in Sonar Images","volume":"44","author":"Abu","year":"2019","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/LGRS.2019.2895843","article-title":"Nonhomogeneous Noise Removal from Side-Scan Sonar Images Using Structural Sparsity","volume":"16","author":"Jin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1109\/TIM.2009.2015520","article-title":"Processing and Analysis of Underwater Acoustic Images Generated by Mechanically Scanned Sonar Systems","volume":"58","author":"Trucco","year":"2009","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1109\/JSEN.2021.3131645","article-title":"Sonar Image Target Detection Based on Adaptive Global Feature Enhancement Network","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Yang, P. (2021). An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal. Remote Sens., 13.","DOI":"10.3390\/rs13245008"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Choi, H.M., Yang, H.S., and Seong, W.J. (2021). Compressive Underwater Sonar Imaging with Synthetic Aperture Processing. Remote Sens., 13.","DOI":"10.3390\/rs13101924"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e6239","DOI":"10.1002\/cpe.6239","article-title":"A Deep Neural Network Learning-based Speckle Noise Removal Technique for Enhancing the Quality of Synthetic-aperture Radar Images","volume":"33","author":"Mohan","year":"2021","journal-title":"Concurr. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172988142093609","DOI":"10.1177\/1729881420936091","article-title":"A Review on the Wavelet Methods for Sonar Image Segmentation","volume":"17","author":"Tian","year":"2020","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/TIM.2007.913703","article-title":"From 3-D Sonar Images to Augmented Reality Models for Objects Buried on the Seafloor","volume":"57","author":"Palmese","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5911","DOI":"10.1109\/JSEN.2022.3149841","article-title":"Side-Scan Sonar Image Segmentation Based on Multi-Channel Fusion Convolution Neural Networks","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rahnemoonfar, M., and Dobbs, D. (August, January 28). Semantic Segmentation of Underwater Sonar Imagery with Deep Learning. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898742"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tian, Y., Lan, L., and Sun, L. (August, January 30). A Review of Sonar Image Segmentation for Underwater Small Targets. Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems, Athens, Greece.","DOI":"10.1145\/3415048.3416098"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"07KG06","DOI":"10.7567\/JJAP.55.07KG06","article-title":"A Prior-Knowledge-Based Threshold Segmentation Method of Forward-Looking Sonar Images for Underwater Linear Object Detection","volume":"55","author":"Liu","year":"2016","journal-title":"Jpn. J. Appl. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5647519","DOI":"10.1155\/2018\/5647519","article-title":"Underwater Acoustic Image Encoding Based on Interest Region and Correlation Coefficient","volume":"2018","author":"Lixin","year":"2018","journal-title":"Complexity"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/JOE.2017.2721058","article-title":"A Framework for Acoustic Segmentation Using Order Statistic-Constant False Alarm Rate in Two Dimensions from Sidescan Sonar Data","volume":"43","author":"Villar","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karine, A., Lasmar, N., Baussard, A., and El Hassouni, M. (2015, January 17\u201320). Sonar Image Segmentation Based on Statistical Modeling of Wavelet Subbands. Proceedings of the 2015 IEEE\/ACS 12th International Conference of Computer Systems and Applications (AICCSA), Marrakech, Morocco.","DOI":"10.1109\/AICCSA.2015.7507134"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1109\/JOE.2018.2835218","article-title":"Classification and Localization of Naval Mines with Superellipse Active Contours","volume":"44","author":"Kohntopp","year":"2019","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1109\/JSEN.2020.3013649","article-title":"A Local Region-Based Level Set Method with Markov Random Field for Side-Scan Sonar Image Multi-Level Segmentation","volume":"21","author":"Li","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/JOE.2018.2819278","article-title":"Segmentation of Sidescan Sonar Imagery Using Markov Random Fields and Extreme Learning Machine","volume":"44","author":"Song","year":"2019","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1109\/TIP.2019.2930148","article-title":"Enhanced Fuzzy-Based Local Information Algorithm for Sonar Image Segmentation","volume":"29","author":"Abu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, H., Lu, W., and Er, M.J. (2020). An Integrated Strategy toward the Extraction of Contour and Region of Sonar Images. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8080595"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, L., Er, M.J., and Yang, Q. (2021, January 14\u201316). Underwater Sonar Image Segmentation Based on Deep Learning of Receptive Field Block and Search Attention Mechanism. Proceedings of the 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS), Wuhan, China.","DOI":"10.1109\/ICoIAS53694.2021.00016"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"928206","DOI":"10.3389\/fnbot.2022.928206","article-title":"Side-Scan Sonar Image Segmentation Based on Multi-Channel CNN for AUV Navigation","volume":"16","author":"Yang","year":"2022","journal-title":"Front. Neurorobot."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, M., Wang, Q., Rigall, E., Li, K., Zhu, W., He, B., and Yan, T. (2019). ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation. Sensors, 19.","DOI":"10.3390\/s19092009"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Iterative, Deep Synthetic Aperture Sonar Image Segmentation","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/JOE.2021.3103269","article-title":"Automatic Target Recognition for Mine Countermeasure Missions Using Forward-Looking Sonar Data","volume":"47","author":"Palomeras","year":"2022","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/JOE.2012.2235664","article-title":"Relocating Underwater Features Autonomously Using Sonar-Based SLAM","volume":"38","author":"Fallon","year":"2013","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7074","DOI":"10.1109\/JSEN.2017.2755547","article-title":"Beam Slice-Based Recognition Method for Acoustic Landmark with Multi-Beam Forward Looking Sonar","volume":"17","author":"Pyo","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Machado, M., Drews, P., Nunez, P., and Botelho, S. (2016, January 8\u201312). Semantic Mapping on Underwater Environment Using Sonar Data. Proceedings of the 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS\/SBR), Recife, Brazil.","DOI":"10.1109\/LARS-SBR.2016.48"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Singh, D., and Valdenegro-Toro, M. (2021, January 11\u201317). The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00417"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/JBHI.2020.2996783","article-title":"MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation from T1-Weighted Magnetic Resonance Images","volume":"25","author":"Zhang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1016\/j.cgh.2018.08.052","article-title":"Project Sonar: A Community Practice-Based Intensive Medical Home for Patients with Inflammatory Bowel Diseases","volume":"16","author":"Singh","year":"2018","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/TCSVT.2017.2654543","article-title":"Residual Networks of Residual Networks: Multilevel Residual Networks","volume":"28","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s11760-020-01841-x","article-title":"Detection and Segmentation of Underwater Objects from Forward-Looking Sonar Based on a Modified Mask RCNN","volume":"15","author":"Fan","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JOE.2019.2950974","article-title":"Real-Time Object Detection for AUVs Using Self-Cascaded Convolutional Neural Networks","volume":"46","author":"Song","year":"2021","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, M., Yu, F., Feng, C., Li, K., Zhu, Y., Rigall, E., and He, B. (2019). RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images. Sensors, 19.","DOI":"10.3390\/s19091985"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TIP.2019.2926748","article-title":"Coarse-to-Fine Semantic Segmentation from Image-Level Labels","volume":"29","author":"Jing","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, 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_48","doi-asserted-by":"crossref","first-page":"9445","DOI":"10.1109\/TIP.2020.3028196","article-title":"SAFNet: A Semi-Anchor-Free Network with Enhanced Feature Pyramid for Object Detection","volume":"29","author":"Jin","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., and Dollar, P. (2019, January 15\u201320). Panoptic Feature Pyramid Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_53","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2014). Object Detectors Emerge in Deep Scene CNNs. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Computer Vision\u2014ECCV 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_57","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., and Luo, P. (2021). SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, T., Wang, W., Li, J., and Yang, Y. (2022, January 18\u201324). Deep Hierarchical Semantic Segmentation. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00131"},{"key":"ref_59","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8468\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:10:13Z","timestamp":1760145013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":59,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218468"],"URL":"https:\/\/doi.org\/10.3390\/s22218468","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,3]]}}}