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The real-time detection of facial skin problems is crucial for improving dermatological health, yet its practical implementation remains challenging. Early detection and timely intervention can significantly enhance skin health while reducing the financial burden associated with traditional dermatological treatments. This paper introduces EM-YOLO, an advanced deep learning framework designed for embedded and distributed environments, leveraging improvements in YOLO models (versions 5, 7, and 8) for high-performance, real-time skin condition detection. The proposed architecture incorporates custom layers, including Squeeze-and-Excitation Block (SEB), Depthwise Separable Convolution (DWC), and Residual Dropout Block (RDB), to optimize feature extraction, enhance model robustness, and improve computational efficiency for deployment in resource-constrained settings. The proposed EM-YOLO model architecture clearly delineates the role of each architectural component, including preprocessing, detection, and postprocessing phases, ensuring a structured and modular representation of the detection pipeline. Extensive experiments demonstrate that EM-YOLO significantly outperforms traditional YOLO models in detecting facial skin conditions such as acne, dark circles, enlarged pores, and wrinkles. The proposed model achieves a precision of 82.30%, recall of 71.50%, F1-score of 76.40%, and [email protected] of 68.80%, which are 23.52%, 32.7%, 29.34%, and 24.68% higher than standard YOLOv8, respectively. Furthermore, the enhanced YOLOv8 custom layers significantly improve system efficiency, achieving a request rate of 15 Req/s with an end-to-end latency of 0.315 s and an average processing latency of 0.021 s, demonstrating 51.61% faster inference and 200% improved throughput compared to traditional SCAS systems. These results highlight EM-YOLO’s superior precision, robustness, and efficiency, making it a highly effective solution for real-time dermatological detection tasks in embedded and distributed computing environments.
