Design and Implementation of Filtering-Based Automatic Extraction System for Real-Time Monitoring of Vehicle Information

Kim, Bum Joon; Han, Dongheon; Baek, Yunju

doi:10.7840/kics.2019.44.9.1778

Cited by 2 publications

(1 citation statement)

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“…‖QT‖ 1 and 2 represent the difference. e result of the control model [21] parameter λ 1 is the fault level of the earth coefficient matrix P, and parameter λ 2 is the fault coefficient of the earth coefficient matrix Q.…”

Section: Yolov3 Algorithmmentioning

confidence: 99%

IOT Monitoring System for Ship Operation Management Based on YOLOv3 Algorithm

Chen

2022

Journal of Control Science and Engineering

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Combined with the YOLOv3 algorithm, the Darknet network developed on the Internet of Things offers boat maintenance, design, and deployment to meet the needs of developing and implementing the Internet of Things-based ship management. System maintenance was completed, solving the problem of care and identifying the vessels in the water important for care. Based on this, the YOLOv3 algorithm has been reported to achieve the target thinking based on the global data map, and the target area thinking and the distribution plan need to be set into a standard neural network. Add a penalty for fixing the boat to different parts of the system together. Binarily divide the needs by a set of logistic regression, allowing rapid tracking and identification of goals in high-risk situations. Experimental results show that the average validation rate of this study’ standard is 89.5% at 30 frames per second. Compared with traditional and in-depth training, this data algorithm is not only more practical and accurate but also more efficient in learning algorithms and various environments. The switches are more flexible and can control multiple ships and their essentials.

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Section: Yolov3 Algorithmmentioning

confidence: 99%