Ship detection deep neural network algorithm based on an image

A deep neural network and ship technology, applied in biological neural network model, neural architecture, computing and other directions, can solve problems such as missed detection, ship collision, and inability to actively report position, and achieve the effect of strong accuracy and rapidity

Pending Publication Date: 2020-04-24
NANTONG UNIVERSITY
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AI Technical Summary

Problems solved by technology

Especially small ships, because they are not equipped with AIS (Automatic Identification System) and other equipment, they cannot actively report their own position and other information, and are not easy to be automatically sensed by other ships, which may easily cause ship collisions
The traditional ship detection

Method used

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  • Ship detection deep neural network algorithm based on an image
  • Ship detection deep neural network algorithm based on an image
  • Ship detection deep neural network algorithm based on an image

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Embodiment

[0031] The present invention is based on the research of convolutional neural network in the field of computer vision, on the basis of feature learning, classification and regression are fused in a deep neural network for multi-target real-time detection, specifically image-based deep neural network algorithm for ship detection (SD -DCNN).

[0032] 1. SD-CDNN target detection process

[0033] Because there are multiple ship targets in the image, each prediction box needs to be discriminated. The specific detection process is:

[0034] In the first step, the image is upsampled to double its length and width, and the initial features are extracted by convolution operation;

[0035] In the second step, the image is divided into S*S grid cells (Grid Cell), and the present invention predicts B bounding boxes (Bounding Boxes) for each grid, and each frame gives 6 parameters, namely X, Y, W, H, SHIPConfidence, SHIPPro, where (X, Y) is the center abscissa of the hull prediction fra...

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Abstract

The invention discloses a ship detection deep neural network algorithm based on an image, and the algorithm comprises the steps: 1, carrying out the up-sampling of the image, and enabling the length and width of the image to be twice of the original length and width; 2, dividing the image into S * S grids, providing B boundary predictions for each grid, and giving six parameters including a ship position, a confidence coefficient and a classification probability for each boundary prediction; 3, extracting the features of each grid through a cascaded hole convolutional neural network, achievingthe multi-resolution ship boundary prediction through feature fusion, and determining the position of a ship; and 4, designing a loss function, and balancing the prediction frame with the ship body part and the prediction frame without the ship body part by setting different scaling factors. Based on the research of the convolutional neural network in the field of computer vision, classificationand regression are fused in one deep neural network for multi-target real-time detection on the basis of feature learning, and the method has very high accuracy and rapidity.

Description

technical field [0001] The invention belongs to the field of ship detection, in particular to an image-based deep neural network algorithm (SD-DCNN) for ship detection. Background technique [0002] Image-based automatic detection of ships is one of the basic issues for automatic and safe driving of ships, and it is also an important issue in the field of computer vision. Especially small ships, because they are not equipped with AIS (Automatic Identification System) and other equipment, they cannot actively report their own position and other information, and are not easy to be automatically sensed by other ships, which may easily cause ship collisions. The traditional ship detection method uses manual features, the accuracy and robustness of the algorithm are not enough, and there will be false detection and missed detection. The existing methods based on deep learning can realize the automatic detection of ships. However, it is easy to miss detection for small ships, whi...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06N3/04
CPCG06V10/25G06N3/045
Inventor 邵叶秦丁政年李志伟马雪仪李杰向阳施佺
Owner NANTONG UNIVERSITY
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