Related Experiment Video
Updated: Aug 12, 2026
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
Published on: July 11, 2025
Frequency-domain attention enhanced YOLOv11-EfficientFormerV2 for Tiny lesion detection in complex field plant images
Shanjiang Zhang1, Renjing Liu1
1School of Management, Xi'an Jiaotong University, Xi'an, China.
Introduction:
To address the challenges of low detection precision, severe background interference, and high model complexity in tiny crop disease lesion detection (defined as lesions occupying 8×8 to 32×32 pixels at 640×640 input resolution) under complex field environments, this study proposes a lightweight detection model named FDA-YOLO by integrating frequency-domain attention and improved YOLOv11.
Methods:
The model employs EfficientFormerV2 as the backbone to extract multi-scale features with low computational cost, and introduces a frequency domain attention module to enhance high-frequency tiny disease lesion details and suppress background noise.
Results:
Comprehensive experiments on the PlantDoc dataset demonstrate that the proposed model achieves 96.3% mAP@0.5, 96.8% precision, and 36.4 FPS with only 28.5M parameters, outperforming the selected baseline detectors under the adopted experimental setting.
Discussion:
The model realizes an optimal balance between accuracy, efficiency, and lightweight performance, providing a reliable and practical solution for real-time tiny lesion detection inprecision agriculture and edge device deployment.