Applicability of object detection to microfossil research: implications from deep learning models to detect microfossil fish teeth and denticles using YOLO-v7

Abstract

Microfossils of fish teeth and denticles, referred to as 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 automatically detect ichthyoliths from microscopic images using a deep learning technique. 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 data set achieved an F1 score of 0.87. We then enhanced the data set 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, denticles and irregularly shaped teeth with minimal human intervention. 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.