{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T02:59:29Z","timestamp":1783565969621,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0301400"],"award-info":[{"award-number":["2016YFC0301400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51809246"],"award-info":[{"award-number":["51809246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2018QF003"],"award-info":[{"award-number":["ZR2018QF003"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Underwater fishing nets represent a danger faced by autonomous underwater vehicles (AUVs). To avoid irreparable damage to the AUV caused by fishing nets, the AUV needs to be able to identify and locate them autonomously and avoid them in advance. Whether the AUV can avoid fishing nets successfully depends on the accuracy and efficiency of detection. In this paper, we propose an object detection multiple receptive field network (MRF-Net), which is used to recognize and locate fishing nets using forward-looking sonar (FLS) images. The proposed architecture is a center-point-based detector, which uses a novel encoder-decoder structure to extract features and predict the center points and bounding box size. In addition, to reduce the interference of reverberation and speckle noises in the FLS image, we used a series of preprocessing operations to reduce the noises. We trained and tested the network with data collected in the sea using a Gemini 720i multi-beam forward-looking sonar and compared it with state-of-the-art networks for object detection. In order to further prove that our detector can be applied to the actual detection task, we also carried out the experiment of detecting and avoiding fishing nets in real-time in the sea with the embedded single board computer (SBC) module and the NVIDIA Jetson AGX Xavier embedded system of the AUV platform in our lab. The experimental results show that in terms of computational complexity, inference time, and prediction accuracy, MRF-Net is better than state-of-the-art networks. In addition, our fishing net avoidance experiment results indicate that the detection results of MRF-Net can support the accurate operation of the later obstacle avoidance algorithm.<\/jats:p>","DOI":"10.3390\/s21061933","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T20:51:42Z","timestamp":1615409502000},"page":"1933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Multiple Receptive Field Network (MRF-Net) for Autonomous Underwater Vehicle Fishing Net Detection Using Forward-Looking Sonar Images"],"prefix":"10.3390","volume":"21","author":[{"given":"Rixia","family":"Qin","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Yang","sequence":"additional","affiliation":[{"name":"College 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":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangliang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1002\/rob.20350","article-title":"Robotic tools for deep water archaeology: Surveying an ancient shipwreck with an autonomous underwater vehicle","volume":"27","author":"Bingham","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_3","unstructured":"Neves, G., Cerqueira, R., Albiez, J., and Oliveira, L. (2019, January 15\u201321). Rotation-invariant shipwreck recognition with forward-looking sonar. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach Convention & Entertainment Center, Los Angeles, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2015.10.122","article-title":"DeepFish: Accurate underwater live fish recognition with a deep architecture","volume":"187","author":"Qin","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kim, J., and Yu, S.-C. (2016, January 6\u20139). Convolutional neural network-based real-time ROV detection using forward-looking sonar image. Proceedings of the 2016 IEEE\/OES Autonomous Underwater Vehicles (AUV), Tokyo, Japan.","DOI":"10.1109\/AUV.2016.7778702"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M. (2019, January 4\u20136). Learning Objectness from Sonar Images for Class-Independent Object Detection. Proceedings of the 2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic.","DOI":"10.1109\/ECMR.2019.8870959"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, T.G., and Choi, H.-T. (November, January 30). Preliminary study on a framework for imaging sonar based underwater object recognition. Proceedings of the 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju, Korea.","DOI":"10.1109\/URAI.2013.6677326"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/48.922790","article-title":"Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar","volume":"26","author":"Petillot","year":"2001","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_9","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_10","unstructured":"Zhou, X., Wang, D., and Krahenbuhl, P. (2019, January 15\u201321). Objects as Points. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach Convention & Entertainment Center, Los Angeles, CA, USA."},{"key":"ref_11","unstructured":"Zhang, Z., He, T., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019, January 15\u201321). Bag of Freebies for Training Object Detection Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach Convention & Entertainment Center, Los Angeles, CA, USA."},{"key":"ref_12","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, San Diego, CA, USA."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (July, January 26). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2018, January 18\u201322). Yolov3: An Incremental Improvement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A.C. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_24","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, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., and Wang, Y. (2018, January 8\u201314). Receptive Field Block Net for Accurate and Fast Object Detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_26","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Weng, L.-Y., Li, M., Gong, Z., and Ma, S. (2012, January 11\u201314). Underwater object detection and localization based on multi-beam sonar image processing. Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China.","DOI":"10.1109\/ROBIO.2012.6491018"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hurtos, N., Palomeras, N., Nagappa, S., and Salvi, J. (2013, January 10\u201314). Automatic detection of underwater chain links using a forward-looking sonar. Proceedings of the 2013 MTS\/IEEE OCEANS\u2014Bergen, Bergen, Norway.","DOI":"10.1109\/OCEANS-Bergen.2013.6608106"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M. (2016, January 18\u201320). Submerged marine debris detection with autonomous underwater vehicles. Proceedings of the 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), Hong Kong, China.","DOI":"10.1109\/RAHA.2016.7931907"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M. (2016, January 6\u20139). End-to-end object detection and recognition in forward-looking sonar images with convolutional neural networks. Proceedings of the 2016 IEEE\/OES Autonomous Underwater Vehicles (AUV), Tokyo, Japan.","DOI":"10.1109\/AUV.2016.7778662"},{"key":"ref_31","unstructured":"Zacchini, L., Franchi, M., Manzari, V., Pagliai, M., and Ridolfi, A. (October, January 30). Forward-Looking Sonar CNN-based Automatic Target Recog-nition: An experimental campaign with FeelHippo AUV. Proceedings of the IEEE Conference on Autonomous Underwater Vehicles Symposium (AUV), St. Johns, NL, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kvasic, I., Miskovic, N., and Vukic, Z. (2019, January 17\u201320). Convolutional Neural Network Architectures for Sonar-Based Diver Detection and Tracking. Proceedings of the OCEANS 2019\u2014Marseille, Marseille, France.","DOI":"10.1109\/OCEANSE.2019.8867461"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., and Sun, J. (2018, January 18\u201322). Detnet: A Backbone network for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1007\/978-3-030-01240-3_21"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201322). Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recog-nition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_35","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017, January 21\u201326). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_36","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pan, X., Luo, P., Shi, J., and Tang, X. (2018). Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. Constructive Side-Channel Analysis and Secure Design, Springer International Publishing.","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). CornerNet: Detecting Objects as Paired Keypoints. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_39","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopezpaz, D. (May, January 30). mixup: Beyond Empirical Risk Minimization. Proceedings of the Inter-national Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_40","unstructured":"Loshchilov, I., and Hutter, F. (2017, January 24\u201326). SGDR: Stochastic Gradient Descent with Warm Restarts. Proceedings of the International Conference on Learning Representations, Toulon, France."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/1933\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:35:53Z","timestamp":1760160953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/1933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,10]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21061933"],"URL":"https:\/\/doi.org\/10.3390\/s21061933","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,10]]}}}