YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation

Abstract

Hair loss affects over 30% of the global population, impacting psychological well-being and social interactions. Robotic hair transplantation has emerged as a pivotal solution, requiring precise hair follicle detection for effective treatment. Traditional methods utilizing horizontal bounding boxes (HBBs) often misclassify due to the follicles’ elongated shapes and varied orientations. This study introduces YOLO-OHFD, a novel YOLO-based method using oriented bounding boxes (OBBs) for improved hair follicle detection in dermoscopic images, addressing the limitations of traditional HBB approaches by enhancing detection accuracy and computational efficiency. YOLO-OHFD incorporates the ECA-Res2Block in its feature extraction network to manage occlusions and hair follicle orientation variations effectively. A Feature Alignment Module (FAM) is embedded within the feature fusion network to ensure precise multi-scale feature integration. We utilize angle classification over regression for robust angle prediction. The method was validated using a custom dataset comprising 500 dermoscopic images with detailed annotations of hair follicle orientations and classifications. The proposed YOLO-OHFD method outperformed existing techniques, achieving a mean average precision (mAP) of 87.01% and operating at 43.67 frames per second (FPS). These metrics attest to its efficacy and real-time application potential. The angle classification component particularly enhanced the stability and precision of orientation predictions, critical for the accurate positioning required in robotic procedures. YOLO-OHFD represents a significant advancement in robotic hair transplantation, providing a robust framework for precise, efficient, and real-time hair follicle detection. Future work will focus on refining computational efficiency and testing in dynamic surgical environments to broaden the clinical applicability of this technology.