Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System
This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based d...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/2/342 |
| _version_ | 1797619237590663168 |
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| author | Jehong An Kwonwook Son Kwanghyun Jung Sangyoo Kim Yoonchul Lee Sangbin Song Jaeyoung Joo |
| author_facet | Jehong An Kwonwook Son Kwanghyun Jung Sangyoo Kim Yoonchul Lee Sangbin Song Jaeyoung Joo |
| author_sort | Jehong An |
| collection | DOAJ |
| description | This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships. |
| first_indexed | 2024-03-11T08:24:17Z |
| format | Article |
| id | doaj.art-73cb49c78d4c44879c5636bccd02eec6 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-11T08:24:17Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-73cb49c78d4c44879c5636bccd02eec62023-11-16T22:10:54ZengMDPI AGMicromachines2072-666X2023-01-0114234210.3390/mi14020342Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control SystemJehong An0Kwonwook Son1Kwanghyun Jung2Sangyoo Kim3Yoonchul Lee4Sangbin Song5Jaeyoung Joo6Lighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of KoreaDepartment of Electrical Engineering, Yeungnam University, Gyeongsan 42415, Republic of KoreaLighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of KoreaLighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of KoreaLighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of KoreaLighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of KoreaLighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of KoreaThis thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships.https://www.mdpi.com/2072-666X/14/2/342computer visionembedded systemdeep learningdehazeLED marine lanternserial communication |
| spellingShingle | Jehong An Kwonwook Son Kwanghyun Jung Sangyoo Kim Yoonchul Lee Sangbin Song Jaeyoung Joo Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System Micromachines computer vision embedded system deep learning dehaze LED marine lantern serial communication |
| title | Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System |
| title_full | Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System |
| title_fullStr | Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System |
| title_full_unstemmed | Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System |
| title_short | Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System |
| title_sort | enhancement of marine lantern s visibility under high haze using ai camera and sensor based control system |
| topic | computer vision embedded system deep learning dehaze LED marine lantern serial communication |
| url | https://www.mdpi.com/2072-666X/14/2/342 |
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