Mr. Hanyu Li Profile

Hanyu Li

at Jilin Univ

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

SPIE Journal Paper | 12 July 2022

Vehicle detection method based on adaptive multi-scale feature fusion network

Xuanjing Shen, Hanyu Li, Yongping Huang, Yu Wang

JEI, Vol. 31, Issue 04, 043008, (July 2022) https://doi.org/10.1117/12.10.1117/1.JEI.31.4.043008

KEYWORDS: Convolution, Feature extraction, Detection and tracking algorithms, Information fusion, Algorithm development, Image fusion, Fusion splicing, Video, Roads, Optimization (mathematics)

Read Abstract +

To improve the vehicle detection accuracy and solve the problem that small vehicles are difficult to detect, an adaptive multi-scale feature fusion network (AMFFN) is proposed to deal with the multi-scale problem, and better performance is achieved after applying it to you only look once (YOLO) v4. To improve the representation capability of features, spatial pyramid pooling modules were employed on each feature map. The proposed AMFFN fuses features of multiple scales across layers and assigns learnable weights to layers of different scales. To achieve detailed information better, we select dynamic rectified linear unit as the activation function, which can change dynamically with the input. AMFFN can be treated as a reusable module to obtain more refined features by repeatedly fusing features. To avoid the huge amount of parameters caused by the complex network, depthwise separable convolution is used to replace the normal convolution and increase the speed of detection. Experimental results show that the proposed method has higher detection accuracy and faster detection speed, and the performance is better than that of YOLO v5, which is the latest version of the YOLO series of algorithms.

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