Yak Object Detection Based on Data Augmentation of Style Transfer Method

To address the issue of complex yak data collection in the plateau area, as well as a lack of data, which leads to the limitation of the object detection model, a data enhancement method based on style transfer is used to increase the number of Tibetan plateau yak samples and improve object detection accuracy. In this study, we examine the results of several generative adversarial networks using the cycle generative adversarial network technique of alternate insertion residual network. Extend the original 450 yak data set by two times, manually generate four different data sets, compare the accuracy of different data sets using the YOLOv3[1] object detection model, and verify that the alternating insertion residual network recurrent generation counter network improves the data effect. The test results suggest that this strategy may significantly enhance item detection accuracy in small samples.