{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T21:33:51Z","timestamp":1785965631067,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T00:00:00Z","timestamp":1660176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xiamen Ocean and Fishery Development Special Fund Project","award":["21CZB013HJ15"],"award-info":[{"award-number":["21CZB013HJ15"]}]},{"name":"Xiamen Ocean and Fishery Development Special Fund Project","award":["B18208"],"award-info":[{"award-number":["B18208"]}]},{"name":"Xiamen Ocean and Fishery Development Special Fund Project","award":["ZP2020042"],"award-info":[{"award-number":["ZP2020042"]}]},{"name":"Xiamen Key Laboratory of Marine Intelligent Terminal R&amp;D and Application","award":["21CZB013HJ15"],"award-info":[{"award-number":["21CZB013HJ15"]}]},{"name":"Xiamen Key Laboratory of Marine Intelligent Terminal R&amp;D and Application","award":["B18208"],"award-info":[{"award-number":["B18208"]}]},{"name":"Xiamen Key Laboratory of Marine Intelligent Terminal R&amp;D and Application","award":["ZP2020042"],"award-info":[{"award-number":["ZP2020042"]}]},{"name":"Fund Project of Jimei University","award":["21CZB013HJ15"],"award-info":[{"award-number":["21CZB013HJ15"]}]},{"name":"Fund Project of Jimei University","award":["B18208"],"award-info":[{"award-number":["B18208"]}]},{"name":"Fund Project of Jimei University","award":["ZP2020042"],"award-info":[{"award-number":["ZP2020042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Due to the abundant natural resources of the underwater world, autonomous exploration using underwater robots has become an effective technological tool in recent years. Real-time object detection is critical when employing robots for independent underwater exploration. However, when a robot detects underwater, its computing power is usually limited, which makes it challenging to detect objects effectively. To solve this problem, this study presents a novel algorithm for underwater object detection based on YOLOv4-tiny to achieve better performance with less computational cost. First, a symmetrical bottleneck-type structure is introduced into the YOLOv4-tiny\u2019s backbone network based on dilated convolution and 1 \u00d7 1 convolution. It captures contextual information in feature maps with reasonable computational cost and improves the mAP score by 8.74% compared to YOLOv4-tiny. Second, inspired by the convolutional block attention module, a symmetric FPN-Attention module is constructed by integrating the channel-attention module and the spatial-attention module. Features extracted by the backbone network can be fused more efficiently by the symmetric FPN-Attention module, achieving a performance improvement of 8.75% as measured by mAP score compared to YOLOv4-tiny. Finally, this work proposed the YOLO-UOD for underwater object detection through the fusion of the YOLOv4-tiny structure, symmetric FPN-Attention module, symmetric bottleneck-type dilated convolutional layers, and label smoothing training strategy. It can efficiently detect underwater objects in an embedded system environment with limited computing power. Experiments show that the proposed YOLO-UOD outperforms the baseline model on the Brackish underwater dataset, with a detection mAP of 87.88%, 10.5% higher than that of YOLOv4-tiny\u2019s 77.38%, and the detection result exceeds YOLOv5s\u2019s 83.05% and YOLOv5m\u2019s 84.34%. YOLO-UOD is deployed on the embedded system Jetson Nano 2 GB with a detection speed of 9.24 FPS, which shows that it can detect effectively in scenarios with limited computing power.<\/jats:p>","DOI":"10.3390\/sym14081669","type":"journal-article","created":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T23:05:49Z","timestamp":1660259149000},"page":"1669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1132-134X","authenticated-orcid":false,"given":"Shijia","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Ocean Information Engineering, Jimei University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiachun","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Ocean Information Engineering, Jimei University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shidan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Ocean Information Engineering, Jimei University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Ocean Information Engineering, Jimei University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/JOE.2017.2786878","article-title":"Deep Image Representations for Coral Image Classification","volume":"44","author":"Mahmood","year":"2019","journal-title":"IEEE J. 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