Mr. Kai Wang Profile

Kai Wang

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 | 10 September 2024 Paper

MCISP-YOLOv5: YOLOv5-based multiscale channel interactive spatially perceptive small object detection algorithm

Jiangtao Guo, Kai Wang, Shu Cao, Wenzhong Yang

Proceedings Volume 13257, 132570N (2024) https://doi.org/10.1117/12.3042714

KEYWORDS: Object detection, Target detection, Convolution, Unmanned aerial vehicles, Detection and tracking algorithms, Small targets, Deformation, Semantics, Visualization, Performance modeling

Read Abstract +

Two central problems often faced when shooting at high altitudes from UAVs are many small and dense targets and complex background noise interference. In YOLOv5, due to multiple downsampling, the feature representation of small targets becomes weaker and may even be masked in the background. The Feature Pyramid Network (FPN) diminishes the detection accuracy of small targets due to its basic feature concatenation method, which underutilizes multiscale information and introduces extraneous contextual details. To solve the above problems, we propose a simple and effective improved model called multiscale channel interactive spatial perception yolov5 (MCISP-YOLOv5). First, we design a multiscale channel interaction spatial perception(MCISP) module, which recalibrates the channel features in each scale by interacting with information from different scales, facilitates the information flow between the shallow feature geometric information, and the more profound feature semantic information, and uses adaptive spatial learning to realize spatial perception so that the model can focus on the foreground objects better. Second, we replace the traditional up-sampling module with the Content-Aware ReAssembly of Features (CARAFE) operator for feature extraction, which enhances the feature characterization ability after multiple down-sampling, better recovers the detailed information. Finally, we added an additional, shallower depth feature map as the detection layer in YOLOv5. The supplementary feature map enhances the detection efficacy for small objects without adverse effects on the detection capabilities for other sizes of targets. Extensive experiments on the publicly available VisDrone2019 dataset show that the introduced model exhibits substantial enhancements in performance.

Proceedings Article | 21 June 2024 Paper

Deep hierarchical multiscale attention networks for image super-resolution

Xinxin Meng, Kai Wang, Shu Cao, Wenzhong Yang

Proceedings Volume 13167, 131672L (2024) https://doi.org/10.1117/12.3029717

KEYWORDS: Super resolution, Performance modeling, Feature fusion, Gallium arsenide, Education and training, Image fusion, Image quality, Transformers, Feature extraction, Image restoration

Read Abstract +

With the rapid development of deep learning, the task of image super-resolution has made significant progress. However, as model depth increases, training becomes more difficult and fails to capture coarse-grained and fine-grained information simultaneously. To solve these issues, we propose Deep Hierarchical Multiscale Attention Networks (DHMA). First, we use a residual nested residual structure to improve the propagation of gradient information to address the challenge of training deep networks, which consists of multiple residual groups, each composed of multiple residual blocks. In addition, we propose a Hierarchical Multiscale Attention (HMA) module to capture both coarse-grained and fine-grained features in deep networks. This method imitates how humans observe things, first focusing on salient objects and then observing details. Specifically, we first segment the feature map horizontally into several parts and then perform Global Aware Attention (GAA) learning on each part. Where GAA can learn global structural information. Next, we use the Adaptive Feature Fusion (AFF) module to fuse the learned information of each part into the current layer's features. Finally, we stack the features of each layer to construct a hierarchical multiscale structure and obtain features of different scales. HMA is a lightweight plug-and-play module that can be applied to existing models. Extensive experiments demonstrate the effectiveness and outstanding performance of the DHMA.

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