KSII Transactions on Internet and Information Systems (TIIS)

  • 제18권10호
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  • Pages.2966-2983
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  • 2024
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Marine Vessel Target Detection Algorithm Based On Improved YOLOv5

  • Chen Gao (School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences)) ;
  • Jiyong Xu (School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences)) ;
  • Ruixia Liu (School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences))
  • 투고 : 2024.05.07
  • 심사 : 2024.09.23
  • 발행 : 2024.10.31
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초록

Considering the intricate and ever-changing nature of the marine environment and the diverse range of sizes for targets involved in marine ship target recognition, which present challenges in detecting specific targets, a marine ship target detection algorithm has been developed based on an enhanced iteration of YOLOv5. Initially, the integration of dynamic snake convolution (DySnakeConv) into the feature extraction network and subsequent enhancement of the C3 module based on this integration were implemented. This integration enables dynamic adjustments based on the input image size, adaptive fusion of feature sequences, and resolution of accuracy and continuity issues during the recognition process. Subsequently, a novel hybrid encoder (FSI) was devised, utilizing target scale characteristics to enhance the extraction capability of multi-scale information, facilitating effective detection and recognition of objects within images. Finally, we selected the Shape-IOU bounding box loss function to mitigate fixed target frame issues and enhance target detection accuracy. Experimental evaluations were conducted utilizing the Infrared Maritime Ship dataset. The results demonstrated that our enhanced model achieved a prediction accuracy of 93.8% and an average precision (mAP) value of 93.89%, surpassing YOLOv8s by 1.2% and 1.8%, respectively. Moreover, there was an increase in recall rate by 2% compared to YOLOv8n while reducing parameters from 10,473,392 to 6,549,901 only. The computational load decreased by 6.3 GFLOps compared with YOLOV8n, resulting in better performance in ocean target detection and recognition.

키워드

과제정보

This work is supported by the Natural Science Foundation Innovation and Development Joint Fund Project of Shandong Province under Grant NO. ZR2023LZH009 and Key R & D Project of Shandong Province under Grant NO. 2020CXGC010501.

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