Technical note: a Recognition-assisted Camera for Automated Microscopy (RaCAM)

Automated microscopy workflows, including image acquisition, processing, and recognition using artificial intelligence (AI), are getting a growing interest from the scientific community in biogeosciences, as more and more research institutes are actively working on building datasets of images to train artificial convolutional neural networks (CNNs) to identify microscopic objects. Here, we present a new, affordable, AI-assisted, and open-source Raspberry Pi-powered camera that can fit any microscope equipped with a regular C-mount (or CS-mount) camera thread. This camera is the first fully self-contained camera equipped with an integrated Single-Board Computer (Raspberry Pi 5) and high-resolution camera sensor (12.3 mp), attached together using a 3D-printable adaptor. Using two new open-source workflows written using the Python language and freely downloadable too, the camera is capable of performing automated microscopy using either an automated image segmentation workflow (RaCAM IS software, including image acquisition, automated image processing, and object recognition using trained ResNet50 CNN models) or an object detection workflow (RaCAM OD software, including image acquisition and automated still image/live stream object detection using YOLO models). The camera is also adapted to on-field tasks such as core description, biostratigraphy or even palaeoenvironmental reconstructions based on microfossils census data or morphometry, as it can operate without the need for a spare computer and run directly on a power bank. Finally, since the automated microscopy workflows rely on images directly captured by the camera, applications can also be extended outside of the microscopy and micropaleontology research fields as any picture acquired with this device can virtually be processed.