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Ship detection method and system combining significance detection and depth learning

A ship detection and deep learning technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve the problem of insufficient detection accuracy and achieve good stability and high robustness

Active Publication Date: 2019-01-18
WUHAN UNIV
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AI Technical Summary

Problems solved by technology

The detection speed of YOLOv2 meets the requirements of real-time detection, but the detection accuracy is not high enough

Method used

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  • Ship detection method and system combining significance detection and depth learning
  • Ship detection method and system combining significance detection and depth learning
  • Ship detection method and system combining significance detection and depth learning

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Embodiment Construction

[0040] In order to better understand the technical solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0041] The present invention provides a model for real-time detection of offshore vessels using depth features and salient features. In order to ensure real-time performance, the model uses the YOLOv2 network in deep learning to predict the category and position of the ship, and uses the salient detection based on the global contrast to correct the ship position to obtain more accurate ship coordinates and ensure the real-time performance of the model. In order to further improve the accuracy, the present invention combines YOLOv2 with saliency detection to obtain more accurate ship positions. The training and experiments of the model are carried out on the ship dataset constructed by ourselves, and very accurate and robust detection results are obtained.

[0042] ...

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Abstract

The invention provides a ship detection method and a ship detection system combining significance detection and depth learning, comprising constructing a ship image sample library, comprising collecting coastal area surveillance video data under visible light, obtaining a frame image containing the ship from the video data, and then obtaining a true value of the ship position, length and width; constructing a YOLO-like convolution neural network to train the video ship target samples and obtaining the training result model of the ship target in the surveillance video; Inputting the ship imagedata and predicting the detection boundary box by using the training result model; by using boundary box information, detecting the saliency to obtain more accurate ship position. The technical schemeof the invention is very fast and efficient, and can achieve the effect of real-time detection. For complex scenes such as clouds and fog, cloudy weather, rain and other conditions, the method can also have good detection results, and has high robustness.

Description

technical field [0001] The invention belongs to the technical field of ship detection based on computer vision, in particular to a ship detection method and system combining saliency detection and deep learning. Background technique [0002] Due to the complex background and the diversity of ship types and sizes, the real-time detection of offshore ships has always been a difficult problem in the military and civilian fields. The real-time nature makes it impossible to use remote sensing and radar images, and only visual images can be used for real-time detection. However, the methods at this stage cannot guarantee real-time and accuracy at the same time. [0003] In order to study how to quickly and accurately detect moving ships from the surveillance video system around the island. Looking at the status quo of ship detection algorithms at home and abroad, we have gradually shifted from traditional methods based on manual extraction of ship feature modeling to Faster RCNN,...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06N3/04
CPCG06V20/52G06V10/25G06N3/045
Inventor 邵振峰王岭钢
Owner WUHAN UNIV
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