Accelerating Pinned Insect Specimen Digitization: A Deep Learning Pipeline for Future Collaborative Robots

The Natural History Museum, UK (NHM), is at the forefront of digitizing vast natural history collections, with over six million of its 80 million specimens already digitized. Extensive, high‐quality, digital specimen datasets are crucial for the integration, and analysis of biological information, providing global accessibility and digital preservation. However, at current rates, it could take centuries to digitize entire collections. To accelerate this, researchers at NHM are exploring the use of collaborative robots (cobots) for digitization. Here, the focus is on the development of artificial intelligence (AI) pipelines for the digitization of one of the largest NHM collections: pinned insects. Aa proof‐of‐concept workflow is presented that leverages AI to assist in precise identification, handling, and digitization of insect specimens and labels. The pipeline is designed to be adaptable across different museum specimen datasets, and to one day integrate seamlessly with the newly introduced cobot at NHM. Experimental results achieved accuracies of 0.95 for specimen identification, 0.79 for pinheads, and 0.92 for specimen labels, in independent image and video test sets. These results demonstrate the potential of this workflow in accelerating digitization efforts whilst prototyping novel cobot‐integrated digitization systems and advancing the biodiversity informatics for data creation and accessibility.