A scalable multi-modal learning fruit detection algorithm for dynamic environments
IntroductionTo enhance the detection of litchi fruits in natural scenes, address challenges such as dense occlusion and small target identification, this paper proposes a novel multimodal target detection method, denoted as YOLOv5-Litchi.MethodsInitially, the Neck layer network of YOLOv5s is simplif...
Saved in:
Main Authors: | Liang Mao, Zihao Guo, Mingzhe Liu, Yue Li, Linlin Wang, Jie Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2025-02-01
|
Series: | Frontiers in Neurorobotics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1518878/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning
by: Zhaoya Gong, et al.
Published: (2025-02-01) -
Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots
by: Xiaojie Shi, et al.
Published: (2025-01-01) -
Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review
by: Zina Ben-Miled, et al.
Published: (2025-01-01) -
MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition
by: S. Raghavendra, et al.
Published: (2025-02-01) -
BEVFusion With Dual Hard Instance Probing for Multimodal 3D Object Detection
by: Taeho Kim, et al.
Published: (2025-01-01)