Abstract

Microfossils of fish teeth and denticles, termed ichthyoliths, provide critical information for depositional ages, paleo-environments and marine ecosystems, especially in pelagic realms. However, owing to their small size and rarity, it is time-consuming and difficult to analyze large numbers of ichthyoliths from sediment samples, limiting their use in scientific studies. Here, we propose a method to detect ichthyoliths from microscopic images automatically using a deep learning technique of object detection. We applied YOLO-v7, one of the latest object detection architectures, and trained several models under different conditions. The model trained under appropriate conditions with an original dataset achieved an F1 score of 0.87. We then enhanced the dataset efficiently using the pre-trained model. We validated the practical applicability of the model by comparing the number of ichthyoliths detected by the model with those counted manually. This revealed that the best model can predict the number of triangular teeth without manual check, and those of denticles and irregularly shaped teeth with manual check. This object detection method can extend the applicability of deep learning to a wider array of microfossils, and has the potential to dramatically increase the spatiotemporal resolution of ichthyolith records for applications across disciplines.