Improved RT-DETR and its application to fruit ripeness detection

Abstract

Introduction: Crop maturity status recognition is a key component of automated harvesting. Traditional manual detection methods are inefficient and costly, presenting a significant challenge for the agricultural industry. Methods: To improve crop maturity detection, we propose enhancements to the Real-Time DEtection TRansformer (RT-DETR) method. The original model's Backbone structure is refined by: HG Block Enhancement: Replacing conventional convolution with the Rep Block during feature extraction, incorporating multiple branches to improve model accuracy. Partial Convolution (PConv): Replacing traditional convolution in the Rep Block with PConv, which applies convolution to only a portion of the input channels, reducing computational redundancy. Efficient Multi-Scale Attention (EMA): Introducing EMA to ensure a uniform distribution of spatial semantic features within feature groups, improving model performance and efficiency. Results: The refined model significantly enhances detection accuracy. Compared to the original model, the average accuracy ([email protected]) improves by 2.9%, while model size is reduced by 5.5% and computational complexity decreases by 9.6%. Further experiments comparing the RT-DETR model, YOLOv8, and our improved model on plant pest detection datasets show that our model outperforms others in general scenarios. Discussion: The experimental results validate the efficacy of the enhanced RT-DETR model in crop maturity detection. The improvements not only enhance detection accuracy but also reduce model size and computational complexity, making it a promising solution for automated crop maturity detection.

Topics & Concepts