{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:35:08Z","timestamp":1782938108834,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,4,28]],"date-time":"2019-04-28T00:00:00Z","timestamp":1556409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.<\/jats:p>","DOI":"10.3390\/s19091985","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T02:57:32Z","timestamp":1556506652000},"page":"1985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images"],"prefix":"10.3390","volume":"19","author":[{"given":"Qi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meihan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaige","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuemei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Rigall","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"He","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,28]]},"reference":[{"key":"ref_1","first-page":"43","article-title":"Side scan sonar for hydrography: An evaluation by the Canadian hydrographic service","volume":"52","author":"Bryant","year":"2015","journal-title":"Int. Hydrogr. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bucci, G. (2018). Remote Sensing and Geo-Archaeological Data: Inland Water Studies for the Conservation of Underwater Cultural Heritage in the Ferrara District, Italy. Remote Sens., 10.","DOI":"10.3390\/rs10030380"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1111\/1556-4029.12671","article-title":"Detecting submerged bodies: Controlled research using side-scan sonar to detect submerged proxy cadavers","volume":"60","author":"Healy","year":"2015","journal-title":"J. Forensic Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/JOE.2017.2686558","article-title":"Efficient seafloor classification and submarine cable route design using an autonomous underwater vehicle","volume":"43","author":"Huang","year":"2017","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fallon, M.F., Kaess, M., Johannsson, H., and Leonard, J.J. (2011, January 9\u201313). Leonard. Efficient auv navigation fusing acoustic ranging and side-scan sonar. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980302"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.oceaneng.2018.04.095","article-title":"Side scan sonar based self-localization for small Autonomous Underwater Vehicles","volume":"161","author":"Petrich","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.oceaneng.2017.06.061","article-title":"Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory","volume":"142","author":"Chabane","year":"2017","journal-title":"Ocean. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TCYB.2016.2530786","article-title":"A robust and fast method for sidescan sonar image segmentation using nonlocal despeckling and active contour model","volume":"47","author":"Huo","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1006\/cviu.1999.0804","article-title":"Three-class markovian segmentation of high-resolution sonar images","volume":"76","author":"Mignotte","year":"1999","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/JOE.2011.2107250","article-title":"A novel method for sidescan sonar image segmentation","volume":"36","author":"Celik","year":"2011","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_11","unstructured":"Liu, G.Y., Bian, H.Y., and Shen, Z.Y. (2019, April 26). Research on level set segmentation algorithm for sonar image. Available online: http:\/\/en.cnki.com.cn\/Article_en\/CJFDTotal-CGQJ201201029.htm."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhu, B., Wang, X., Chu, Z., Yang, Y., and Shi, J. (2019). Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image. Remote Sens., 11.","DOI":"10.3390\/rs11030243"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_16","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_19","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201312). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, J., Liu, Q., and Zhang, K. (2017, January 21\u201326). Stacked hourglass network for robust facial landmark localisation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.253"},{"key":"ref_22","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (2015). Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ren, Q., Geng, J., Ding, M., and Li, J. (2018). Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images. Sensors, 18.","DOI":"10.3390\/s18103232"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, G., Bian, H., Ye, X., and Shi, H. (2011, January 22\u201325). An improved spectral clustering sonar image segmentation method. Proceedings of the The 2011 IEEE\/ICME International Conference on Complex Medical Engineering, Harbin, China.","DOI":"10.1109\/ICCME.2011.5876787"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.oceaneng.2010.03.003","article-title":"Sonar image segmentation based on gmrf and level-set models","volume":"37","author":"Ye","year":"2010","journal-title":"Ocean Eng."},{"key":"ref_26","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, H., Gao, Y., Yu, F., and Darrell, T. (2017, January 21\u201326). End-to-end learning of driving models from large-scale video datasets. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.376"},{"key":"ref_30","unstructured":"Wong, J.M., Wagner, S., Lawson, C., Kee, V., Hebert, M., Rooney, J., and Johnson, D. (2017). Segicp-dsr: Dense semantic scene reconstruction and registration. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation.  CoRR. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_34","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., and Reid, I. (2018). Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations. arXiv.","DOI":"10.1109\/ICRA.2019.8794220"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018). Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. arXiv.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_39","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_40","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/1985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:47:43Z","timestamp":1760186863000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/1985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,28]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19091985"],"URL":"https:\/\/doi.org\/10.3390\/s19091985","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,28]]}}}