Intelligent identification of Cenozoic spore and pollen fossils in Bohai Sea area

The identification of paleontological fossil types and their distribution provides important information for geochronological, paleoenvironmental studies, and oil and gas exploration. However, traditional fossil identification methods are time-consuming, labor-intensive, and highly dependent on manual efforts, making it difficult to meet the current demand for rapid exploration and evaluation. Given the limited number of spore and pollen fossil images, the complex classification of taxa, and the specific taxonomy of family, genus, and species, this research focused on improving the automation for fossil image processing, image screening, object detection, and classification. By utilizing techniques such as deep learning for object detection and label smoothing, the efficiency of fossil screening and spore and pollen fossil classification was significantly enhanced. Taking the identification of the Cenozoic spore and pollen fossils from the Bohai Sea shallow area as a case study, a set of intelligent identification methods was developed using neural networks such as YOLOv5 and DenseNet, with an average identification accuracy of 94%, basically meeting the practical accuracy requirements for fossil identification in production. The system could assist in the manual identification of paleontological fossils. By effectively combining various deep learning techniques with specialized knowledge in paleontology, the generalization ability and recognition accuracy of the identification model were improved from both data and model perspectives. Its successful application demonstrates the feasibility of artificial intelligence in the traditional field of paleontological fossil identification, reducing time and labor costs while providing accurate results.