Oriented Object Detection with RGB-D Data for Corn Pose Estimation

Precise oriented object detection of corn provides critical support for automated agricultural tasks such as harvesting, spraying, and precision management. In this work, we address this challenge by leveraging oriented object detection in combination with depth information to estimate corn poses. To enhance detection accuracy while maintaining computational efficiency, we construct a precise annotated oriented corn detection dataset and propose YOLOv11OC, an improved detector. YOLOv11OC integrates three key components: Angle-aware Attention Module for angle encoding and orientation perception, Cross-Layer Fusion Network for multi-scale feature fusion, and GSConv Inception Network for efficient multi-scale representation. Together, these modules enable accurate oriented detection while reducing model complexity. Experimental results show that YOLOv11OC achieves 97.6% [email protected], exceeding YOLOv11 by 3.2%, and improves mAP50:95 by 5.0%. Furthermore, when combined with depth maps, the system achieves 92.5% pose estimation accuracy, demonstrating its potential to advance intelligent and automated cultivation and spraying.