Weakly Supervised Real-Time Object Detection Based on Salient Map Extraction and the Improved YOLOv5 Model
In order to improve the accuracy and processing speed of object detection in weakly supervised learning environment, a weakly supervised real-time object detection method based on saliency map extraction and improved YOLOv5 is proposed. For the case where only image-level annotations are available,...
Saved in:
Main Authors: | Yue Ma, Zhuangzhi Zhi |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Advances in Multimedia |
Online Access: | http://dx.doi.org/10.1155/2022/1239337 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Cross refinement network with edge detection for salient object detection
by: Junjiang Xiang, et al.
Published: (2021-09-01) -
Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection
by: Suthir Sriram, et al.
Published: (2025-01-01) -
Multiscale Deep Network with Centerness-Aware Loss for Salient Object Detection
by: Liangliang Duan
Published: (2022-01-01) -
RGB-D salient object detection based on BC2 FNet network
by: WANG Feng, et al.
Published: (2024-12-01) -
Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images
by: Xiaoning Zhang, et al.
Published: (2025-01-01)