Facial Blemish Detection Based on YOLOv8n Optimised with Space-to-Depth and GCNet Attention Mechanisms

Facial blemishes are small and often similar in colour to the surrounding skin, making detection even more challenging. This paper proposes an improved algorithm based on YOLOv8 to address the limitations of the original YOLOv8n in facial blemish detection. First, we introduce space-to-depth-convolution (SPD-Conv), which replaces traditional downsampling methods in convolutional neural networks, preserving spatial details without reducing the image resolution. This enhances the model's ability to detect small imperfections. Additionally, the integration of GCNet helps detect blemishes that closely resemble surrounding skin tones by leveraging global context modelling. The improved model better understands the overall structure and features of the face. Experimental results show that our model achieves a 5.3% and 5.6% improvement in mAP50 and mAP50-95, respectively, over YOLOv8n. Furthermore, it outperforms the latest YOLOv11n model by 6.9% and 7.2% in mAP50 and mAP50-95.