التفاصيل البيبلوغرافية
| العنوان: |
YOLOv8-CB: Dense Pedestrian Detection Algorithm Based on In-Vehicle Camera. |
| المؤلفون: |
Liu, Qiuli, Ye, Haixiong, Wang, Shiming, Xu, Zhe |
| المصدر: |
Electronics (2079-9292); Jan2024, Vol. 13 Issue 1, p236, 16p |
| مصطلحات موضوعية: |
PEDESTRIANS, CASCADE connections, VISUAL fields, ALGORITHMS, MULTISCALE modeling, ROAD interchanges & intersections |
| مستخلص: |
Recently, the field of vehicle-mounted visual intelligence technology has witnessed a surge of interest in pedestrian detection. Existing algorithms for dense pedestrian detection at intersections face challenges such as high computational weight, complex models that are difficult to deploy, and suboptimal detection accuracy for small targets and highly occluded pedestrians. To address these issues, this paper proposes an improved lightweight multi-scale pedestrian detection algorithm, YOLOv8-CB. The algorithm introduces a lightweight cascade fusion network, CFNet (cascade fusion network), and a CBAM attention module to improve the characterization of multi-scale feature semantics and location information, and it superimposes a bidirectional weighted feature fusion path BIFPN structure to fuse more effective features and improve pedestrian detection performance. It is experimentally verified that compared with the YOLOv8n algorithm, the accuracy of the improved model is increased by 2.4%, the number of model parameters is reduced by 6.45%, and the computational load is reduced by 6.74%. The inference time for a single image is 10.8 ms. The cascade fusion algorithm YOLOv8-CB has higher detection accuracy and is a lighter model for multi-scale pedestrian detection in complex scenes such as streets or intersections. This proposed algorithm presents a valuable approach for device-side pedestrian detection with limited computational resources. [ABSTRACT FROM AUTHOR] |
|
Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder