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Surv."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n            Underwater optical object detection (UOD), aiming at identifying and localising objects in underwater optical images or videos, presents significant challenges due to the optical distortion, water turbidity, and changing illumination in underwater scenes. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD. To further facilitate future advancements, we comprehensively study AI-based UOD. In this survey, we first categorise existing algorithms into traditional machine learning-based methods and deep learning-based methods, and summarise them by considering learning strategies, experimental datasets, learning stages, employed features or techniques, and underlying frameworks. Next, we discuss the potential challenges and suggest possible solutions and new directions. We also perform both quantitative and qualitative evaluations of mainstream algorithms across multiple benchmark datasets, taking into account the diversity and biases in experimental setups. Finally, we introduce two off-the-shelf detection analysis tools, Diagnosis and TIDE, which will examine the effects of object characteristics and various types of errors on detector performance. These tools help identify the strengths and weaknesses of different detectors, providing insights for further improvement. The source code, trained models, utilised datasets, detection results, and detection analysis tools are publicly available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/LongChenCV\/UODReview\">https:\/\/github.com\/LongChenCV\/UODReview<\/jats:ext-link>\n            and will be regularly updated.\n          <\/jats:p>","DOI":"10.1145\/3759243","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:13:22Z","timestamp":1754565202000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Underwater Optical Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8552-859X","authenticated-orcid":false,"given":"Long","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, University College London","place":["London, United Kingdom of Great Britain and Northern Ireland"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4896-9342","authenticated-orcid":false,"given":"Yuzhi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology","place":["Dalian, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7012-2087","authenticated-orcid":false,"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[{"name":"Ocean University of China","place":["Qingdao, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9245-5544","authenticated-orcid":false,"given":"Qi","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology","place":["Dalian, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7484-7261","authenticated-orcid":false,"given":"Sam","family":"Kwong","sequence":"additional","affiliation":[{"name":"School of Data Science, Lingnan University","place":["Hong Kong, Hong Kong"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9794-3221","authenticated-orcid":false,"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Automation and the Advanced Institute of Ocean, Southeast University","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6668-9758","authenticated-orcid":false,"given":"Huchuan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Dalian University of Technology","place":["Dalian, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2609-2460","authenticated-orcid":false,"given":"Chongyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nankai University","place":["Tianjin, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00703"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/OCEANSChennai45887.2022.9775438"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/OCEANS.2006.306810"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13091854"},{"key":"e_1_3_2_6_2","first-page":"346","article-title":"Surf: Speeded up robust features","author":"Bay Herbert","year":"2006","unstructured":"Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. 2006. 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