YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11

作者：Yue Liao, Lerong Li, Hui Xiao, Feijian Xu, Bochen Shan, Hua Yin · 发表于：Agronomy · 年份：2025 · DOI：10.3390/agronomy15030687 · 被引用次数：66

Accurate quantification of the citrus dropped number plays a vital role in evaluating the disaster resistance capabilities of citrus varieties and selecting superior cultivars. However, research in this critical area remains notably insufficient. To bridge this gap, we conducted in-depth experiments using a custom dataset of 1200 citrus images and proposed a lightweight YOLO-MECD model that is built upon the YOLOv11s architecture. Firstly, the EMA attention mechanism was introduced as a replacement for the traditional C2PSA attention mechanism. This modification not only enhances feature extraction capabilities and detection accuracy for citrus fruits but also achieves a significant reduction in model parameters. Secondly, we implemented a CSPPC module based on partial convolution to replace the original C3K2 module, effectively reducing both parameter count and computational complexity while maintaining mAP values. At last, the MPDIoU loss function was employed, resulting in improved bounding box detection accuracy and accelerated model convergence. Notably, our research reveals that reducing convolution operations in the backbone architecture substantially enhances small object detection capabilities and significantly decreases model parameters, proving more effective than the addition of small object detection heads. The experimental results and comparative analysis with similar network models indicate that the YOLO-MECD model has achieved significant improvements in both ...