Ship detection method in bridge collision avoidance system

Abstract

The invention discloses a ship detection method in a bridge collision avoidance system. The ship detection method includes the steps of carrying out camera calibration using the Zhang Zhengyou checkerboard calibration method; carrying out image acquisition and preprocessing; drawing a virtual channel; detecting a moving foreground by combining a Gaussian mixture model background method and a three-frame difference method; extracting the eigenvalue of a moving foreground target as sample data for training a deep neural network DNN; constructing, training and testing the DNN; identifying characteristics of a ship in a real-time video stream according to the trained DNN and marking the moving ship; and regularly monitoring the sailing condition of the ship according to calibration results. The ship detection method based on the GMM and the three-frame difference method designed by the invention overcomes the problems of a broken foreground contour and an incomplete target in a traditional method, and can accurately and intelligently identify the ship by training the DNN with foreground ship characteristics, thereby improving the accuracy, efficiency and real-time performance of the ship detection.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a ship detection method in a bridge anti-collision system. Background technique [0002] In recent years, the number of ships in inland waterways has been increasing day by day, and inland waterways are crowded. Hundreds of water traffic safety accidents inevitably occur every year, causing immeasurable property and personnel losses. The bridge anti-collision warning system came into being. The moving ship recognition algorithm plays a very important role in the bridge anti-collision warning system. Early warning to ensure that ships pass through the bridge navigation port smoothly and avoid accidents such as ship-bridge collisions. Therefore, a reliable moving ship recognition algorithm is of great significance for ship navigation monitoring. [0003] At present, there are mainly three types of methods for detecting moving targets in static backgrounds at home and abr...

AI Technical Summary

Problems solved by technology

[0003] At present, there are mainly three types of methods for detecting moving targets in static backgrounds at home and abroad: target visual detection algorithm based on background difference method, frame difference method, optical flow method, etc., but only these three types of methods are used to detect ships. There are problems such as incomplete targets and broken contours

When observing a ship from a distance, due to the perspective effect, its motion speed appears to be slow, and the difference between adjacent frames of the video sequence is small, resulting in missed detection by the system

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