{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:26:14Z","timestamp":1781763974171,"version":"3.54.5"},"reference-count":24,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019YFD0900805"],"award-info":[{"award-number":["2019YFD0900805"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42176175"],"award-info":[{"award-number":["42176175"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2019YFD0900805"],"award-info":[{"award-number":["2019YFD0900805"]}]},{"name":"National Natural Science Foundation of China","award":["42176175"],"award-info":[{"award-number":["42176175"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the common problems, such as noise pollution, low contrast, and color distortion in underwater images, and the characteristics of holothurian recognition, such as morphological ambiguity, high similarity with the background, and coexistence of special ecological scenes, this paper proposes an underwater holothurian target-detection algorithm (FA-CenterNet), based on improved CenterNet and scene feature fusion. First, to reduce the model\u2019s occupancy of embedded device resources, we use EfficientNet-B3 as the backbone network to reduce the model\u2019s Params and FLOPs. At the same time, EfficientNet-B3 increases the depth and width of the model, which improves the accuracy of the model. Then, we design an effective FPT (feature pyramid transformer) combination module to fully focus and mine the information on holothurian ecological scenarios of different scales and spaces (e.g., holothurian spines, reefs, and waterweeds are often present in the same scenario as holothurians). The co-existing scene information can be used as auxiliary features to detect holothurians, which can improve the detection ability of fuzzy and small-sized holothurians. Finally, we add the AFF module to realize the deep fusion of the shallow-detail and high-level semantic features of holothurians. The results show that the method presented in this paper yields better results on the 2020 CURPC underwater target-detection image dataset with an AP50 of 83.43%, Params of 15.90 M, and FLOPs of 25.12 G compared to other methods. In the underwater holothurian-detection task, this method improves the accuracy of detecting holothurians with fuzzy features, a small size, and dense scene. It also achieves a good balance between detection accuracy, Params, and FLOPs, and is suitable for underwater holothurian detection in most situations.<\/jats:p>","DOI":"10.3390\/s22197204","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:07:55Z","timestamp":1663888075000},"page":"7204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0682-9157","authenticated-orcid":false,"given":"Yanling","family":"Han","sequence":"first","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Luo","sequence":"additional","affiliation":[{"name":"Guangdong Feida Transportation Engineering Co., Ltd., Guangzhou 510663, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Ai","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-1066","authenticated-orcid":false,"given":"Zhonghua","family":"Hong","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6327-7726","authenticated-orcid":false,"given":"Zhenling","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-9808","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schoening, T., Bergmann, M., Ontrup, J., Taylor, J., Dannheim, J., Gutt, J., Purser, A., and Nattkemper, T.W. (2012). Semi-automated image analysis for the assessment of megafaunal densities at the arctic deep-sea observatory HAUSGARTEN. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0038179"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fabic, J.N., Turla, I.E., Capacillo, J.A., David, L.T., and Naval, P.C. (2013, January 5\u20138). Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. Proceedings of the 2013 IEEE International Underwater Technology Symposium (UT), Tokyo, Japan.","DOI":"10.1109\/UT.2013.6519876"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ecoinf.2013.10.002","article-title":"Real-world underwater fish recognition and identification, using sparse representation","volume":"23","author":"Hsiao","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.compag.2017.02.008","article-title":"An automatic active contour method for sea cucumber segmentation in natural underwater environments","volume":"135","author":"Qiao","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.measurement.2018.10.039","article-title":"fvUnderwater sea cucumber identification based on principal component analysis and support vector machine","volume":"133","author":"Qiao","year":"2019","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_6","unstructured":"Li, X., Shang, M., Qin, H., and Chen, L. (2015, January 19\u201322). Fast accurate fish detection and recognition of underwater images with fast R-CNN. Proceedings of the OCEANS 2015-MTS\/IEEE Washington, Washington, DC, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zurowietz, M., Langenk\u00e4mper, D., Hosking, B., Ruhl, H.A., and Nattkemper, T.W. (2018). MAIA-A machine learning assisted image annotation method for environmental monitoring and exploration. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207498"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"043013","DOI":"10.1117\/1.JEI.29.4.043013","article-title":"Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm","volume":"29","author":"Shi","year":"2020","journal-title":"J. Electron. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, H., Song, P., and Ding, R. (2020). WQT and DG-YOLO: Towards domain generalization in underwater object detection. arXiv.","DOI":"10.1109\/ICIP40778.2020.9191364"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xu, S., Song, W., He, Q., and Wei, Q. (2021). Lightweight underwater object detection based on YOLO v4 and multi-scale attentional feature fusion. Remote Sens., 13.","DOI":"10.3390\/rs13224706"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101786","DOI":"10.1016\/j.ecoinf.2022.101786","article-title":"Fast and accurate mapping of fine scale abundance of a VME in the deep sea with computer vision","volume":"71","author":"Piechaud","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lei, F., Tang, F., and Li, S. (2022). Underwater target detection algorithm based on improved YOLOv5. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10030310"},{"key":"ref_13","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_15","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","article-title":"CornerNet: Detecting objects as paired keypoints","volume":"128","author":"Law","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., and Kr\u00e4henb\u00fchl, P. (2019, January 15\u201320). Bottom-up object detection by grouping extreme and center points. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (2019, January 27\u201328). CenterNet: Keypoint triplets for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 27\u201328). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_20","unstructured":"Tan, M., and Le, Q.V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, H., Tang, J., Wang, M., Hua, X., and Sun, Q. (2020). Feature pyramid transformer. Computer Vision\u2014ECCV 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-58604-1_20"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2017). Non-local neural networks. arXiv.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 4\u20138). Attentional feature fusion. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A.C. (2016). SSD: Single shot MultiBox detector. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46448-0_2"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7204\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:37:45Z","timestamp":1760143065000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,22]]},"references-count":24,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197204"],"URL":"https:\/\/doi.org\/10.3390\/s22197204","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,22]]}}}