Abstract: In complex stage scenes, the accuracy of actor detection is severely affected by color bias and uneven illumination problems generated by multiple light sources. To solve these problems, an actor detection method based on pseudo-multimodal fusion is proposed. First, a random illumination method is used to construct an enhanced image to form a pseudo-multimodal image pair with the original image; second, an actor key point candidate set is created in the enhanced image, and some key points are randomly selected from the set to construct enhanced patches, and the patches are replaced into the original image for training. Then, based on the traditional feature pyramid network, the visual attention module is used to construct visual attention encoders to enhance the interaction logic of multi-scale features. On a self-constructed dataset of 4543 images which containing stage actors, the average accuracy of detection is improved 0.4% to 2.9% by combining the three models, and the results show that the proposed method can better reduce the effects of color bias and uneven illumination.