The Southern sea otter (Enhydra lutris nereis) is a keystone predator and a protected marine mammal that inhabits the California coast and beyond. Current methods to study otters are expensive, inaccurate, and inefficient. To improve the use of resources for government officials, ecologists, and other researchers, we are proposing an auto-detection algorithm using drone aerial imagery as inputs. Using 842 images of sea otters from Monterey Bay and 1018 background images extracted from other open course oceanographic publications, we labeled, clustered, and augmented images to train YOLOv5 with a modified architecture for tiny objects. The resulting model with a 75% F1-score and 76% mAp_0.2:0.5. Future work includes adding background images from newly acquired datasets, using a GAN model to generate images, and using a two-stage detector to improve results.