Ship image target detection method based on deep learning

A technology for ship image and target detection, applied in the field of deep learning and computer vision, can solve the problem of no improvement in image target recognition inside the candidate bounding box.

Active Publication Date: 2020-11-10
HARBIN ENG UNIV
View PDF9 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In general, S-CNN can be regarded as an R-CNN optimized by a general method, which has a great improvement in the generation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Ship image target detection method based on deep learning
  • Ship image target detection method based on deep learning
  • Ship image target detection method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0091] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0092] Such as figure 1 Shown is the network structure diagram of the present invention. First, use the pixel attention model to preprocess the ship image, then use the K-Means clustering algorithm to generate the anchor frame of the ship target and convert the label bounding box, then build the YOLOV3 network based on the feature attention model, and use the training optimization The method is used to train the network, and finally the non-maximum value suppression is used to post-process the predicted output of the network to avoid the problem of repeated detection, so as to realize the detection and recognition of ship targets.

[0093] A kind of ship target detection and recognition method based on deep learning of the present invention comprises the following steps:

[0094] S1: Preprocessing the ship image through the pixel at...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a ship target detection and recognition method based on deep learning, and the method comprises the following steps: S1, building a pixel attention model, and preprocessing a ship image; S2, generating a ship anchoring frame by using KMeans clustering, and converting a label boundary frame; S3, building a YOLOV3 network structure based on a pixel attention model; S4, training a network by using a training optimization method; and S5, performing post-processing on network output by using a non-maximum suppression algorithm to avoid the problem of repeated detection. According to the ship target detection and recognition method based on deep learning, ship target detection and recognition can be realized under various complex backgrounds and resolutions, and the ship target detection and recognition method has a good application prospect in the fields of ship industry, maritime affair management and the like.

Description

technical field [0001] The invention relates to a deep learning and target detection technology, in particular to a deep learning-based ship image target detection method, which belongs to the field of deep learning and computer vision. Background technique [0002] Ship target detection and recognition methods can be divided into three strategies, including end-to-end network structure, two-stage network structure and improved network structure based on the former two. For the end-to-end ship target detection and recognition network structure, Ling Ziqin, Chang Yang-Lang and Wang Bingde directly used YOLOV1, YOLOV2 and YOLOV3 networks to realize ship target detection and recognition, but the network effect could not meet the engineering use standard. Xia Ye and others built a ship target detection and recognition system using the SSD network, and achieved a certain improvement in the detection accuracy of the network, but sacrificed the real-time performance of the network....

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/23213G06F18/214
Inventor 孟浩魏宏巍袁菲闫天昊周炜昊邓艳琴
Owner HARBIN ENG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap