MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9.

ABSTRACT: With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. Existing technologies still face performance bottlenecks when dealing with diverse scenarios. To address these issues, this study proposes a lightweight, cross-dataset enhanced object detection method for the agricultural domain based on YOLOv9, named Multi-Adapt Recognition-YOLOv9 (MAR-YOLOv9). The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively design

SUBMITTER: Lu D 

PROVIDER: S-EPMC11521258 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature