Mr. Zhonghong Wu Profile

Zhonghong Wu

at Naval Univ of Engineering

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 January 2020 Paper

Sea surface ships detection method of UAV based on improved YOLOv3

Xiangfu Zhang, Zhangsong Shi, Zhonghong Wu, Jian Liu

Proceedings Volume 11373, 113730T (2020) https://doi.org/10.1117/12.2557479

KEYWORDS: Target detection, Detection and tracking algorithms, Data modeling, Unmanned aerial vehicles, Convolution, Neural networks, Image processing, Feature extraction, Image enhancement, Visualization

Read Abstract +

For satellite remote sensing image, ship detection is affected by factors such as cloud, weather and sea clutter, and there are problems such as high false alarm rate and missed detection rate. A deep neural network (DT-YOLO) for real-time detection of surface ships in unmanned aerial vehicle (UAV) aerial photography is proposed. DT-YOLO firstly improves the traditional YOLOv3 algorithm by constructing a deep neural network using densely connected modules and transition modules to extract ship features and designs five different scale convolution feature maps. After upsampling, they are merged with the corresponding scale feature maps to form a multi-scale feature pyramid to perform ship prediction tasks. Then the k-means algorithm is used to cluster the target frame dimensions, determine the target frame parameters, and improve the positioning accuracy of the model; In the detection network, the non-maximum suppression algorithm (NMS) is optimized by attenuating the confidence score linearly, which effectively mitigates the problem of mutual occlusion detection of ships. Finally, the self-constructed sea surface ship dataset is established to test the performance of the algorithm in different scenes by using multi-scale training and data enhancement strategies. The results show that the proposed method improves the detection accuracy under the premise of ensuring real-time performance, especially in the detection of larger targets and mutually occluded ships.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years