{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:50:17Z","timestamp":1783572617027,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the 2023 University-Local Integration Develop-ment Project of Yantai City","award":["2323013-2023XDRH001"],"award-info":[{"award-number":["2323013-2023XDRH001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the issues of false positives and missed detections commonly observed in traditional underwater seafood object detection algorithms, this paper proposes an improved detection method based on YOLOv5s. Specifically, we introduce a Spatial\u2013Channel Synergistic Attention (SCSA) module after the Fast Spatial Pyramid Pooling layer in the backbone network. This module adopts a synergistic mechanism where the channel attention guides spatial localization, and the spatial attention feeds back to optimize channel weights, dynamically enhancing the unique features of aquatic targets (such as sea cucumber folds) while suppressing seawater background interference. In addition, we replace some C3 modules in YOLOv5s with our designed three-scale convolution dual-path variable-kernel module based on Pinwheel-shaped Convolution (C3k2-PSConv). This module strengthens the model\u2019s ability to capture multi-dimensional features of aquatic targets, especially in the feature extraction of small-sized and occluded targets, reducing the false detection rate while ensuring the model\u2019s lightweight property. The enhanced model is evaluated on the URPC dataset, which contains real-world underwater imagery of echinus, starfish, holothurian, and scallop. The experimental results show that compared with the baseline model YOLOv5s, while maintaining real-time inference speed, the proposed method in this paper increases the mean average precision (mAP) by 2.3% and reduces the number of parameters by approximately 2.4%, significantly improving the model\u2019s operational efficiency.<\/jats:p>","DOI":"10.3390\/s25247546","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:30:17Z","timestamp":1765528217000},"page":"7546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Seafood Object Detection Method Based on Improved YOLOv5s"],"prefix":"10.3390","volume":"25","author":[{"given":"Nan","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Physics and Electronic, Yantai University, Yantai 264005, China"},{"name":"Shandong Provincial Data Open Innovation Application Laboratory for Advanced Smart Grid Technologies, Yantai University, Yantai 264005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic, Yantai University, Yantai 264005, China"},{"name":"Shandong Provincial Data Open Innovation Application Laboratory for Advanced Smart Grid Technologies, Yantai University, Yantai 264005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongxun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic, Yantai University, Yantai 264005, China"},{"name":"Shandong Provincial Data Open Innovation Application Laboratory for Advanced Smart Grid Technologies, Yantai University, Yantai 264005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic, Yantai University, Yantai 264005, China"},{"name":"Shandong Provincial Data Open Innovation Application Laboratory for Advanced Smart Grid Technologies, Yantai University, Yantai 264005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"129866","DOI":"10.1016\/j.neucom.2025.129866","article-title":"SCSA: Exploring the synergistic effects between spatial and channel attention","volume":"634","author":"Si","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_2","unstructured":"Yang, J., Liu, S., Wu, J., Su, X., Hai, N., and Huang, X. (March, January 25). Pinwheel-shaped convolution and scale-based dynamic loss for infrared small target detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_3","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_4","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the CVPR 2005 IEEE, San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/asmb.537","article-title":"A tutorial on \u03bd-support vector machines","volume":"21","author":"Chen","year":"2005","journal-title":"Appl. Stoch. Models Bus. Ind."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_7","unstructured":"Spampinato, C., Chen-Burger, Y.H., Nadarajan, G., and Fisher, R.B. (2008). Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos, SciTePress."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Oliver, K., Hou, W., and Wang, S. (2010, January 5\u20136). Image feature detection and matching in underwater conditions. Proceedings of the Ocean Sensing and Monitoring II, Orlando, FL, USA.","DOI":"10.1117\/12.852339"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Susanto, T., Mardiyanto, R., and Purwanto, D. (2018, January 26\u201327). Development of underwater object detection method base on color feature. Proceedings of the CENIM 2018 IEEE, Surabaya, Indonesia.","DOI":"10.1109\/CENIM.2018.8711290"},{"key":"ref_10","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 CVPR 2014 IEEE, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the ICCV 2015 IEEE, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_12","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the ICCV 2017 IEEE, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_14","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 CVPR 2016 IEEE, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the CVPR 2017 IEEE, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_16","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_17","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the CVPR 2023 IEEE, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2017, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the ECCV 2016 IEEE, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","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 ICCV 2017 IEEE, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.patcog.2019.03.014","article-title":"Real-time underwater fish target detection based on improved YOLO and transfer learning","volume":"32","author":"Li","year":"2019","journal-title":"Pattern Recognit. Artif. Intell."},{"key":"ref_23","first-page":"394","article-title":"Marine organism recognition algorithm based on improved YOLOv3 network","volume":"37","author":"Liu","year":"2020","journal-title":"Comput. Appl. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fan, B., Chen, W., Cong, Y., and Tian, J. (2020, January 23\u201328). Dual refinement underwater object detection network. Proceedings of the ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58565-5_17"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115306","DOI":"10.1016\/j.eswa.2021.115306","article-title":"S-FPN: A shortcut feature pyramid network for sea cucumber detection in underwater images","volume":"182","author":"Peng","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1051\/jnwpu\/20224020433","article-title":"Underwater target detection algorithm based on channel attention and feature fusion","volume":"40","author":"Zhang","year":"2022","journal-title":"J. Northwest. Polytech. Univ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, S., Wang, Z., Liu, S., Han, Y., Sun, P., and Li, J. (2024). Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm. Sensors, 24.","DOI":"10.3390\/s24237640"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ye, H., and Shu, X. (2025). BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model. Sensors, 25.","DOI":"10.3390\/s25133890"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luo, Y., Wu, A., and Fu, Q. (2025). MAS-YOLOv11: An Improved Underwater Object Detection Algorithm Based on YOLOv11. Sensors, 25.","DOI":"10.3390\/s25113433"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7546\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:32:13Z","timestamp":1765528333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7546"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":29,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25247546"],"URL":"https:\/\/doi.org\/10.3390\/s25247546","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]}}}