{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T14:25:50Z","timestamp":1774362350913,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T00:00:00Z","timestamp":1547596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based on the characteristics of Field-Programmable Gate Array (FPGA), low power consumption, strong computing capability, and high flexibility, we design an embedded FPGA image recognition system on Convolutional Neural Network (CNN). By using two technologies of FPGA, parallelism and pipeline, the parallelization of multi-depth convolution operations is realized. In the experimental phase, we collect and segment the images from underwater video recorded by the submersible. Next, we join the tags with the images to build the training set. The test results show that the proposed FPGA system achieves the same accuracy as the workstation, and we get a frame rate at 25 FPS with the resolution of 1920 \u00d7 1080. This meets our needs for underwater identification tasks.<\/jats:p>","DOI":"10.3390\/s19020350","type":"journal-article","created":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T03:02:03Z","timestamp":1547694123000},"page":"350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Minghao","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Chengquan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Fenglin","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Chong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.dsr2.2013.02.007","article-title":"Getting the bigger picture: Using precision Remotely Operated Vehicle (ROV) videography to acquire high-definition mosaic images of newly discovered hydrothermal vents in the Southern Ocean","volume":"92","author":"Marsh","year":"2013","journal-title":"Deep Sea Res. 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