Facial Expression Detection Algorithm Based on YOLOv8

Facial expression detection is pivotal for the development of affective computing and human-computer interaction, but existing algorithms often fall short in real-time performance, accuracy, and complexity. This paper presents YOLOv8CRGD, a lightweight facial expression detection algorithm based on YOLOv8. The algorithm features a lightweight cross-scale feature fusion module (CCFM) to enhance the model's adaptability to scale variations and a SENetv2 module to improve feature representation, thereby increasing detection accuracy. To address efficiency in complex visual tasks, an improved visual Transformer structure with a dynamic attention mechanism (DAT) is adopted. Furthermore, GhostConv replaces traditional convolution operations to achieve a lightweight model design. Experimental results show that YOLOv8CRGD achieves a 91.3% accuracy and an mAP50 of 94.4% in facial expression detection tasks, while reducing parameters by 15% compared to the YOLOv8n model. With a frame rate (FPS) of 57.9, the algorithm not only maintains high detection accuracy but also excels in real-time performance, making it a compelling candidate for real-time facial expression analysis applications.