Prof. Zhuhua Hu Profile

Prof. Zhuhua Hu

at Hainan Univ

SPIE Involvement:

Author

Publications (2)

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 | 12 October 2022 Paper

RF2Net: salient object detection using level set loss and reverse attention fusion feed network

Yutong Dai, Chong Zhang, Zhuhua Hu, Yaochi Zhao

Proceedings Volume 12342, 123420M (2022) https://doi.org/10.1117/12.2643360

KEYWORDS: Convolution, Feature extraction, Image segmentation, Target detection, Reverse modeling, Performance modeling, Image fusion, Detection and tracking algorithms, Visualization, Visual process modeling

Read Abstract +

Salient object detection is a fundamental problem in the research of image and vision. However, traditional models have low confidence and low recall. Although deep learning methods can better locate objects, the boundaries are often not detailed enough. To address these issues, we propose a salient object detection model (RF2Net) that combines traditional level set methods with deep learning. RF2Net incorporates the idea of level set structured loss and reverse attention mechanism on the basis of F3Net. First, RF2Net uses a new loss function that combines BCE(Binary Cross Entropy) loss, weight level set loss and weight MAE(Mean Absolute Error) loss with multi-indicator joint supervision. Through the role of the level set loss operator, it is possible to better focus on the whole of the image instead of pixel-by-pixel supervision like the BCE loss. The introduction of the reverse attention mechanism can effectively reduce the noise during feature fusion between layers and achieve the purpose of improving accuracy. The experiments are compared with 12 state-of-the-art methods on 4 datasets, and MAE, maxF and avgF are all higher than other algorithms in HKU-IS dataset. At the same time, we also conduct ablation experiments on the DUTS dataset and the ECSSD dataset to verify the effectiveness of the algorithm. The ablation experimental results show that the proposed algorithm can effectively improve the effect of salient object detection.

Proceedings Article | 15 February 2022 Paper

Accurate segmentation of remote sensing cages based on U-Net and voting mechanism

Chuang Yu, Yunpeng Liu, Zhuhua Hu, Xin Xia

Proceedings Volume 12166, 121662T (2022) https://doi.org/10.1117/12.2615946

KEYWORDS: Image segmentation, Remote sensing, Image fusion, Image resolution, Binary data, Statistical modeling, Ocean optics, Data modeling, Lithium, Image processing

Read Abstract +

In aquaculture, the normal growth of fish is closely related to the density of aquaculture. Therefore, it is of great significance to use remote sensing images to accurately segment the cages in a specific sea area at a macro level. This research proposes an accurate segmentation scheme for remote sensing cages based on U-Net and voting mechanism. Firstly, a remote sensing cage segmentation (RSCS) data set is produced, which includes fifty-three high-resolution cage images with inconsistent resolution. Secondly, by using random cropping and data enhancement operations on the training samples, three training sets with image block sizes of 256×256 pixels, 512×512 pixels, and 1024×1024 pixels are created. And through the introduction of U-Net network, three training sets of different sample sizes are trained separately and three trained models are generated. Then, after reasonably filling the test image, a window sliding overlap cropping method is adopted. The high-resolution remote sensing cage test images are sequentially cut into the image blocks for segmentation, and the segmented image blocks are spliced and combined into the binary segmentation image by the mean method. Finally, for each image, the three binary segmentation images generated by different trained models are used to vote for each pixel. The experimental results show that by testing three remote sensing images of Li'an Port, Xincun Port and Potou Port, the Mean Intersection over Union (mIoU) is 0.865. Our data and code can be available online.

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