YOLO-FCE: A feature and clustering enhanced object detection model for species classification

Australia harbours a rich and unique diversity of wildlife, constituting a vital component of the nation’s ecological heritage. Accurate species identification in expansive and remote natural environments remains a significant challenge. In this study, we propose YOLO-Feature and Clustering Enhanced (YOLO-FCE), an improved model based on the YOLOv9 architecture. We conducted a series of cluster-distance-based analyses to evaluate and enhance the model’s feature extraction capabilities. The proposed model was trained and tested on a dataset containing 50 Australian animal species, with 700 images per species, resulting in a total of 35,000 images. YOLO-FCE achieved a mean Average Precision (mAP50:95) of 87.5% and a precision of 98.2%. On a separate validation set of previously unseen images, it attained a recognition accuracy of 91.29% with an average confidence score of 0.801. Compared with baseline models including YOLOv9, YOLOv11, and Faster R-CNN evaluated on the same dataset, YOLO-FCE demonstrated robust performance.