MSDP-Net: A YOLOv5-Based Safflower Corolla Object Detection and Spatial Positioning Network

Abstract

In response to the challenge of low detection and positioning accuracy for safflower corollas during field operations, we propose a deep learning-based object detection and positioning algorithm called the Mobile Safflower Detection and Position Network (MSDP-Net). This approach is designed to overcome issues related to the small size of safflower corollas and their tendency to be occluded in complex agricultural environments. For object detection, we introduce an improved YOLO v5m model, referred to as C-YOLO v5m, which integrates a Convolutional Block Attention Module (CBAM) into both the backbone and neck networks. This modification enhances the model’s ability to focus on key features, resulting in increases in the precision, recall, and mean average precision of 4.98%, 4.3%, and 5.5%, respectively. For spatial positioning, we propose a mobile camera-based method in which a binocular camera is mounted on a translation stage, enabling horizontal movement that maintains optimal positioning accuracy and mitigates occlusion issues. Field experiments demonstrate that this mobile positioning method achieves a success rate of 93.79% with average deviations of less than 3 mm in the X, Y, and Z directions. Moreover, comparisons with five mainstream object detection algorithms reveal that MSDP-Net offers superior overall performance, making it highly suitable for safflower corolla detection. Finally, when applied to our self-developed safflower harvesting robot, 500 indoor trial tests achieved a harvest success rate of 90.20%, and field tests along a 15 m row confirmed a success rate above 90%, thereby validating the effectiveness of the proposed methods.