Bounding box versus point annotation: The impact on deep learning performance for animal detection in aerial images

Abstract

Bounding box and point annotations are widely used in deep learning-based animal detection from remote sensing imagery, yet their impact on model performance and training efficiency remains insufficiently explored. This study systematically evaluates the influence of these two annotation methods using aerial survey datasets of African elephants and antelopes across three commonly employed deep learning networks: YOLO, CenterNet, and U-Net. In addition, we assess the effect of image spatial resolution and the training efficiency associated with each annotation method. Our findings indicate that when using YOLO, there is no statistically significant difference in model accuracy between bounding box and point annotations. However, for CenterNet and U-Net, bounding box annotations consistently yield significantly higher accuracy compared to point-based annotations, with these trends remaining consistent across different spatial resolution ranges. Furthermore, training efficiency varies depending on the network and annotation method. While YOLO exhibits similar convergence speeds for both annotation types, U-Net models trained with bounding box annotations converge significantly faster, followed by CenterNet, where bounding box-based models also show improved convergence. These findings demonstrate that the choice of annotation method should be guided by the specific deep learning architecture employed. While point-based annotations are more cost-effective, their lower training efficiency in U-Net and CenterNet suggests that bounding box annotations are preferable when maximizing both accuracy and computational efficiency. Therefore, when selecting annotation strategies for animal detection in remote sensing applications, researchers should carefully balance detection accuracy, annotation cost, and training efficiency to optimize performance for specific task requirements.