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Ship target detection method based on deep learning

A target detection and deep learning technology, applied in image data processing, instruments, biological neural network models, etc., can solve the problems of poor real-time detection and high computing resource requirements

Pending Publication Date: 2021-09-10
DALIAN MARITIME UNIVERSITY
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Problems solved by technology

[0013] In order to solve the problem of high computing resource requirements and poor real-time detection of deep convolutional neural network, and comprehensively consider the application scenarios of ship characteristics and auxiliary navigation and monitoring, the present invention provides a ship target detection method based on improved YOLOv3

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  • Ship target detection method based on deep learning
  • Ship target detection method based on deep learning
  • Ship target detection method based on deep learning

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[0051] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0052] When the YOLOv3 algorithm was proposed, it was used to detect 80 types of targets in the public data set. However, when detecting a single type of target such as a ship, there are parameter redundancy, which easily leads to overfitting. In addition, the length and width of the ship are relatively large, and YOLOv3 has a regre...

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Abstract

The invention provides a ship target detection method based on deep learning, and relates to the technical field of target detection. The method comprises the steps: acquiring a ship data set; carrying out image splicing on the ship images in the ship data set; constructing a ship target detection model, wherein the ship target detection model comprises a lightweight backbone network and an attention pyramid, the lightweight backbone network comprises an input layer, an average pooling layer and a dense connection unit, the attention pyramid comprises three detection branches which are respectively connected to dense connection units for outputting 13 x 13, 26 x 26 and 52 x 52 dimension feature maps in the backbone network, the attention pyramid uses effective channel attention modules which are located at the foremost end of the attention pyramid, and each detection branch is provided with one effective channel attention module; and performing ship target detection by using the constructed ship target detection model to obtain a ship detection result. According to the invention, more accurate, lightweight and real-time ship detection is realized.

Description

technical field [0001] The invention relates to the technical field of target detection, in particular to a deep learning-based ship target detection method. Background technique [0002] With the improvement of port throughput and the increase in the number of ships, effective ship-assisted navigation and monitoring methods are becoming more and more important. As the current mainstream auxiliary navigation and monitoring equipment, AIS and radar provide guarantee for the navigation and supervision of ships. At the same time, due to the rapid development of computer vision technology, the accuracy and real-time performance of ship target detection are gradually improving, and it can be used as a supplementary technology for AIS and radar to provide richer information. In addition, with the gradual development of unmanned ship technology, ship target detection, as an environmental perception method, provides a new information acquisition method for the autonomous planning a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40G06T7/62G06T7/10G06N3/04
CPCG06T3/4038G06T7/62G06T7/10G06N3/045
Inventor 潘明阳赵丽宁李哲林李邵喜李超郝江凌胡景峰刘宗鹰张若澜孙慧李航琪
Owner DALIAN MARITIME UNIVERSITY
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