Abstract

In this paper, we focus on the safety supervision of inland vessels. This paper especially aims at studying the vessel target detection and dynamic tracking algorithm based on computer vision and the target fusion algorithm based on multisensor. For the vessel video target detection and tracking, this paper analyzes the current widely used methods and theories. Additionally, facing the application scenarios and characteristics of inland vessels, a comprehensive vessel video target detection algorithm is proposed in this paper. It is combined with a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling. Besides, for the multisensor target fusion, the processing method of laser point cloud data and automatic identification system (AIS) data is analyzed in this paper. Based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction. Finally, through this method, a set of vessel situation active intelligent perception systems based on multisensor fusion was developed. Experiments show that this method has better environmental applicability and detection accuracy than traditional manual detection and any single monitoring method.

Highlights

  • With the rapid development of inland shipping, the number of water transportation vessels is increasing, and the pressure on water transportation is rising

  • In view of the previously mentioned problems in vessel video target detection, this paper proposes a vessel video target detection algorithm combining a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling, which effectively improve the success rate of video target detection

  • This paper analyzes the processing method of laser point cloud data and automatic identification system (AIS) data, and based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction

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Summary

Introduction

With the rapid development of inland shipping, the number of water transportation vessels is increasing, and the pressure on water transportation is rising. Relying solely on manual labor for on-site or video inspections has low efficiency, and intelligent supervision methods are still insufficient. For the intelligent supervision of vessels, video, AIS, RFID, radar, and other technologies are mainly used currently. Ere is no unified and efficient fusion method to discover accurate and Mathematical Problems in Engineering valuable information from multisource disordered supervision data. How to accurately and effectively extract ship targets from existing videos to achieve automatic object detection and tracking of vessel video targets and cooperate with multisensor multisource data fusion analysis to improve the level of supervision precision and realize the intuitive expression of vessel supervision is currently a problem that needs to be solved urgently

Research Overview
Vessel Video Target Detection and Dynamic Tracking
Multisensor Target Fusion Algorithm
Test Verification Analysis
Conclusions
Full Text
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