An Object Detection Algorithm for Military Vehicles Based on Image Style Transfer and Domain Adversarial Learning

In order to improve the accuracy of object detection based on deep learning for military vehicles., an object detection algorithm based on image style transfer and domain adversarial learning is proposed. Aiming at the problem of the small number of military vehicle images., a military vehicle image dataset is constructed by using games., miniature models and spider technology. However., due to the visual difference between these images., the accuracy of the trained detection model is poor when it is directly used to detect the actual collected images. CycleGAN is used for image style transfer to obtain images with a similar style to military vehicle images at the data level. Domain adversarial learning is used to optimize the one-stage object detection algorithm to make the network learn domain invariant features and reduce the domain discrepancy at the feature level. The proposed algorithm is implemented with YOLOv5s as an example. The test results show that the proposed algorithm improves the average precision (AP) by 5.7 ^% without increasing the amount of inference computation., compared with the YOLOv5s algorithm.