Kavli Affiliate: Ting Xu
| First 5 Authors: Weijie He, Yuwei Zhang, Ting Xu, Tai An, Yingbin Liang
| Summary:
Deep learning has emerged as a transformative approach for solving complex
pattern recognition and object detection challenges. This paper focuses on the
application of a novel detection framework based on the RT-DETR model for
analyzing intricate image data, particularly in areas such as diabetic
retinopathy detection. Diabetic retinopathy, a leading cause of vision loss
globally, requires accurate and efficient image analysis to identify
early-stage lesions. The proposed RT-DETR model, built on a Transformer-based
architecture, excels at processing high-dimensional and complex visual data
with enhanced robustness and accuracy. Comparative evaluations with models such
as YOLOv5, YOLOv8, SSD, and DETR demonstrate that RT-DETR achieves superior
performance across precision, recall, mAP50, and mAP50-95 metrics, particularly
in detecting small-scale objects and densely packed targets. This study
underscores the potential of Transformer-based models like RT-DETR for
advancing object detection tasks, offering promising applications in medical
imaging and beyond.
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