{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:30:28Z","timestamp":1774121428497,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100016106","name":"Jiangsu Provincial Industry Prospective and Key Core Technology Project","doi-asserted-by":"publisher","award":["BE2021135"],"award-info":[{"award-number":["BE2021135"]}],"id":[{"id":"10.13039\/501100016106","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016106","name":"Jiangsu Provincial Industry Prospective and Key Core Technology Project","doi-asserted-by":"publisher","award":["GJ2020009"],"award-info":[{"award-number":["GJ2020009"]}],"id":[{"id":"10.13039\/501100016106","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008868","name":"Zhenjiang International Science and Technology Cooperation Project","doi-asserted-by":"publisher","award":["BE2021135"],"award-info":[{"award-number":["BE2021135"]}],"id":[{"id":"10.13039\/501100008868","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008868","name":"Zhenjiang International Science and Technology Cooperation Project","doi-asserted-by":"publisher","award":["GJ2020009"],"award-info":[{"award-number":["GJ2020009"]}],"id":[{"id":"10.13039\/501100008868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The ocean encompasses the majority of the Earth\u2019s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues of subpar image quality and low recognition accuracy. The precise measures are enumerated as follows: initially, to address the issue of model parameters, we optimized the ninth convolutional layer by substituting certain conventional convolutions with adaptive deformable convolution DCN v4. This modification aims to more effectively capture the deformation and intricate features of underwater targets, while simultaneously decreasing the parameter count and enhancing the model\u2019s ability to manage the deformation challenges presented by underwater images. Furthermore, the Triplet Attention module is implemented to augment the model\u2019s capacity for detecting multi-scale targets. The integration of low-level superficial features with high-level semantic features enhances the feature expression capability. The original CIoU loss function was ultimately substituted with Shape IoU, enhancing the model\u2019s performance. In the underwater robot grasping experiment, the system shows particular robustness in handling radial symmetry in marine organisms and reflection symmetry in artificial structures. The enhanced algorithm attained a mean Average Precision (mAP) of 87.6%, surpassing the original YOLOv8s model by 3.4%, resulting in a marked enhancement of the object detection model\u2019s performance and fulfilling the real-time detection criteria for underwater robots.<\/jats:p>","DOI":"10.3390\/sym17071102","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T07:38:27Z","timestamp":1752133107000},"page":"1102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improvement and Optimization of Underwater Image Target Detection Accuracy Based on YOLOv8"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4190-0193","authenticated-orcid":false,"given":"Yisong","family":"Sun","sequence":"first","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5309-1507","authenticated-orcid":false,"given":"Tianzhong","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, L., Zheng, M., Duan, S., Luo, W., and Yao, L. 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