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Research and Application of Panoramic Visual Perception-Assisted Navigation Technology for Ships

Gorde:
Argitaratua izan da:Journal of Marine Science and Engineering vol. 12, no. 7 (2024), p. 1042
Egile nagusia: Wang, Chiming
Beste egile batzuk: Cai, Xiaocong, Li, Yanan, Zhai, Runxuan, Wu, Rongjiong, Zhu, Shunzhi, Guan, Liangqing, Luo, Zhiqiang, Zhang, Shengchao, Zhang, Jianfeng
Argitaratua:
MDPI AG
Gaiak:
Stitching
Navigation
Accuracy
Visual perception
Radar detection
Tugboats
Algorithms
Regression models
Radar equipment
Target detection
Visual fields
Registration
Image quality
Perception
Visual perception driven algorithms
Radar
Shipping industry
Field of view
Environmental
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Laburpena:In response to challenges such as narrow visibility for ship navigators, limited field of view from a single camera, and complex maritime environments, this study proposes panoramic visual perception-assisted navigation technology. The approach includes introducing a region-of-interest search method based on SSIM and an elliptical weighted fusion method, culminating in the development of the ship panoramic visual stitching algorithm SSIM-EW. Additionally, the YOLOv8s model is improved by increasing the size of the detection head, introducing GhostNet, and replacing the regression loss function with the WIoU loss function, and a perception model yolov8-SGW for sea target detection is proposed. The experimental results demonstrate that the SSIM-EW algorithm achieves the highest PSNR indicator of 25.736, which can effectively reduce the stitching traces and significantly improve the stitching quality of panoramic images. Compared to the baseline model, the YOLOv8-SGW model shows improvements in the P, R, and mAP50 of 1.5%, 4.3%, and 2.3%, respectively, its mAP50 is significantly higher than that of other target detection models, and the detection ability of small targets at sea has been significantly improved. Implementing these algorithms in tugboat operations at ports enhances the fields of view of navigators, allowing for the identification of targets missed by AISs and radar systems, thus ensuring operational safety and advancing the level of vessel intelligence.
ISSN:2077-1312
DOI:10.3390/jmse12071042
Baliabidea:Engineering Database