Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation
Merchant ships sometimes fail to detect small ships at night and in poor visibility, leading to urgent situations and even collisions. Infrared (IR) cameras have inherent advantages in small target detection and become essential environmental awareness equipment on unmanned ships. The existing targe...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/6/1114 |
| _version_ | 1827736925327327232 |
|---|---|
| author | Zongjiang Gao Yingjun Zhang Shaobo Wang |
| author_facet | Zongjiang Gao Yingjun Zhang Shaobo Wang |
| author_sort | Zongjiang Gao |
| collection | DOAJ |
| description | Merchant ships sometimes fail to detect small ships at night and in poor visibility, leading to urgent situations and even collisions. Infrared (IR) cameras have inherent advantages in small target detection and become essential environmental awareness equipment on unmanned ships. The existing target detection models are complex and difficult to deploy on small devices. Lightweight detection algorithms are needed with the increase in the number of shipborne cameras. Therefore, herein, a lightweight model for small IR ship detection was selected as the research object. IR videos were collected in the Bohai Strait, the image sampling interval was calculated, and an IR dataset of small ships was constructed. Based on the analysis of the characteristics of the IR ship images, gamma transform was used to preprocess the images, which increased the gray difference between the target and background. The backbone of YOLOv5 was replaced with that of Mobilev3 to improve the computing efficiency. Finally, the results showed that the parameters of the proposed model were reduced by 83% compared with those of the YOLOv5m model, while the detection performance was almost the same. |
| first_indexed | 2024-03-11T02:17:55Z |
| format | Article |
| id | doaj.art-f71b9a5bd1ce4281814ef8f8649ac8df |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T02:17:55Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-f71b9a5bd1ce4281814ef8f8649ac8df2023-11-18T11:06:06ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-05-01116111410.3390/jmse11061114Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous NavigationZongjiang Gao0Yingjun Zhang1Shaobo Wang2Navigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaMerchant ships sometimes fail to detect small ships at night and in poor visibility, leading to urgent situations and even collisions. Infrared (IR) cameras have inherent advantages in small target detection and become essential environmental awareness equipment on unmanned ships. The existing target detection models are complex and difficult to deploy on small devices. Lightweight detection algorithms are needed with the increase in the number of shipborne cameras. Therefore, herein, a lightweight model for small IR ship detection was selected as the research object. IR videos were collected in the Bohai Strait, the image sampling interval was calculated, and an IR dataset of small ships was constructed. Based on the analysis of the characteristics of the IR ship images, gamma transform was used to preprocess the images, which increased the gray difference between the target and background. The backbone of YOLOv5 was replaced with that of Mobilev3 to improve the computing efficiency. Finally, the results showed that the parameters of the proposed model were reduced by 83% compared with those of the YOLOv5m model, while the detection performance was almost the same.https://www.mdpi.com/2077-1312/11/6/1114small ship detectiongamma transforminfrared targetdeep learning |
| spellingShingle | Zongjiang Gao Yingjun Zhang Shaobo Wang Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation Journal of Marine Science and Engineering small ship detection gamma transform infrared target deep learning |
| title | Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation |
| title_full | Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation |
| title_fullStr | Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation |
| title_full_unstemmed | Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation |
| title_short | Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation |
| title_sort | lightweight small ship detection algorithm combined with infrared characteristic analysis for autonomous navigation |
| topic | small ship detection gamma transform infrared target deep learning |
| url | https://www.mdpi.com/2077-1312/11/6/1114 |
| work_keys_str_mv | AT zongjianggao lightweightsmallshipdetectionalgorithmcombinedwithinfraredcharacteristicanalysisforautonomousnavigation AT yingjunzhang lightweightsmallshipdetectionalgorithmcombinedwithinfraredcharacteristicanalysisforautonomousnavigation AT shaobowang lightweightsmallshipdetectionalgorithmcombinedwithinfraredcharacteristicanalysisforautonomousnavigation |