 This paper proposes a novel approach using YOLOv5, an advanced and robust object detection model, to detect and classify pressure ulcers into four stages and non-pressure ulcers. Data augmentation was used to expand the dataset and strengthen the resilience of the model. The proposed approach achieved an overall mean average precision of 76.9% with class-specific map 50 values ranging from 66% to 99.5%. This compares favorably to other approaches which use CNN-based algorithms. The successful implementation of this approach could lead to improved early detection and treatment of pressure ulcers resulting in better patient outcomes and reduced health care costs. This article was authored by Bader Aldegafik, Farzinesh Fouk, N. Z. Janji, and others.