CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13396 > Article
Paper
24 October 2024 An improved stamen target detection method based on YOLOv8
Ruihua Zhang, Sunxin Wang, Yu Liang, Yuchen Wang, Rundong Huang
Author Affiliations +
Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960T (2024) https://doi.org/10.1117/12.3050406
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China
Abstract
Aiming at the problem of difficulty in extracting key features caused by complex background information and many types of scenes and targets in the stamen image, the Multi-Head Self-Attention (MHSA) module is added to the YOLOv8 backbone network, which improves the ability of the backbone network to extract key features. To address the challenges of significant target scale variations and the presence of small targets in stamen images, we introduce a specialized small target detection layer. This enhancement allows the model to focus more on detecting small targets, thereby improving overall detection performance. By strengthening the YOLOv8 model's capabilities in small target detection and enabling efficient fusion of multi-scale and multi-layer features extracted by the backbone network, the detection performance is significantly enhanced. We conducted ablation experiments, and the experiment results show that the proposed algorithm improves the recognition precision and recall indexes by 2.7% and 2.9% respectively. Additionally, the mAP50 value reaches 99.3%, meeting the requirements for real-time accurate positioning of stamens.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Ruihua Zhang, Sunxin Wang, Yu Liang, Yuchen Wang, and Rundong Huang "An improved stamen target detection method based on YOLOv8", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960T (24 October 2024); https://doi.org/10.1117/12.3050406
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Target detection
Detection and tracking algorithms
Small targets
Feature extraction
Deep learning
Target recognition
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top