YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9

Abstract

Invasive alien plants (IAPs) present a significant threat to ecosystems and agricultural production, necessitating rigorous monitoring and detection for effective management and control. To realize accurate and rapid detection of invasive alien plants in the wild, we proposed a rapid detection approach grounded in an advanced YOLOv9, referred to as YOLO-IAPs, which incorporated several key enhancements to YOLOv9, including replacing the down-sampling layers in the model’s backbone with a DynamicConv module, integrating a Triplet Attention mechanism into the model, and replacing the original CIoU with the MPDloU. These targeted enhancements collectively resulted in a substantial improvement in the model’s accuracy and robustness. Extensive training and testing on a self-constructed dataset demonstrated that the proposed model achieved an accuracy of 90.7%, with the corresponding recall, mAP50, and mAP50:95 measured at 84.3%, 91.2%, and 65.1%, and a detection speed of 72 FPS. Compared to the baseline, the proposed model showed increases of 0.2% in precision, 3.5% in recall, and 1.0% in mAP50. Additionally, YOLO-IAPs outperformed other state-of-the-art object detection models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10 series, Faster R-CNN, SSD, CenterNet, and RetinaNet, demonstrating superior detection capabilities. Ablation studies further confirmed that the proposed model was effective, contributing to the overall improvement in performance, which underscored its pre-eminence in the domain of invasive alien plant detection and offered a marked improvement in detection accuracy over traditional methodologies. The findings suggest that the proposed approach has the potential to advance the technological landscape of invasive plant monitoring.