Research on facial expression recognition based on improved YOLOv8

The work pressure on offshore platforms in the western oil fields of the South China Sea is high and in urgent need of humanistic care. Aiming at problems such as the difficulty in extracting micro-features by ordinary convolution operations in facial expression recognition and how to effectively fuse multi-scale features, the YOLOv8 algorithm is applied to the facial expression recognition task. YOLOv8 introduces new backbone networks such as C2f module, spatial pyramid pooling structure and multi-scale feature fusion to enhance image feature extraction capabilities, and more effectively guides the model to learn useful features during the training process by optimizing the loss function. In addition, the ability to extract data features is improved by introducing SPD-conv convolution operation. Experimental results on the FER2013 data set show that the improved YOLOv8 algorithm is effective. The recognition accuracy rates of Accuracy_top1 and Accuracy_top5 reached 69.72% and 99.31% respectively.