GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising
Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/6/11/335 |
| _version_ | 1827646815367856128 |
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| author | Yongshuai Li Haiwen Yuan Yanfeng Wang Changshi Xiao |
| author_facet | Yongshuai Li Haiwen Yuan Yanfeng Wang Changshi Xiao |
| author_sort | Yongshuai Li |
| collection | DOAJ |
| description | Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel object detection algorithm for drone cruising in large-scale maritime scenarios. Transformer is introduced to enhance the feature extraction part and is beneficial to small or occluded object detection. Meanwhile, the computational cost of the algorithm is reduced by replacing the convolution operations with simpler linear transformations. To illustrate the performance of the algorithm, a specialized dataset composed of thousands of images collected by drones in maritime scenarios is given, and quantitative and comparative experiments are conducted. By comparison with other derivatives, the detection precision of the algorithm is increased by 1.4%, the recall is increased by 2.6% and the average precision is increased by 1.9%, while the parameters and floating-point operations are reduced by 11.6% and 7.3%, respectively. These improvements are thought to contribute to the application of drones in maritime and other remote sensing fields. |
| first_indexed | 2024-03-09T19:08:45Z |
| format | Article |
| id | doaj.art-c82692f1d7e64a87b920a1a3606ce759 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T19:08:45Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-c82692f1d7e64a87b920a1a3606ce7592023-11-24T04:22:03ZengMDPI AGDrones2504-446X2022-10-0161133510.3390/drones6110335GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime CruisingYongshuai Li0Haiwen Yuan1Yanfeng Wang2Changshi Xiao3School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaNational Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, ChinaDrones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel object detection algorithm for drone cruising in large-scale maritime scenarios. Transformer is introduced to enhance the feature extraction part and is beneficial to small or occluded object detection. Meanwhile, the computational cost of the algorithm is reduced by replacing the convolution operations with simpler linear transformations. To illustrate the performance of the algorithm, a specialized dataset composed of thousands of images collected by drones in maritime scenarios is given, and quantitative and comparative experiments are conducted. By comparison with other derivatives, the detection precision of the algorithm is increased by 1.4%, the recall is increased by 2.6% and the average precision is increased by 1.9%, while the parameters and floating-point operations are reduced by 11.6% and 7.3%, respectively. These improvements are thought to contribute to the application of drones in maritime and other remote sensing fields.https://www.mdpi.com/2504-446X/6/11/335dronemaritime surveillanceobject detectionTransformerGhostNet |
| spellingShingle | Yongshuai Li Haiwen Yuan Yanfeng Wang Changshi Xiao GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising Drones drone maritime surveillance object detection Transformer GhostNet |
| title | GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising |
| title_full | GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising |
| title_fullStr | GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising |
| title_full_unstemmed | GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising |
| title_short | GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising |
| title_sort | ggt yolo a novel object detection algorithm for drone based maritime cruising |
| topic | drone maritime surveillance object detection Transformer GhostNet |
| url | https://www.mdpi.com/2504-446X/6/11/335 |
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