Military target detection method based on EfficientDet and Generative Adversarial Network

Abstract

Military target identification is one of the first tasks of modern counter-terrorism operations, and military target detection methods based on unmanned system platforms can effectively reduce personnel casualties and improve combat effectiveness. Due to the complexity and variability of the actual combat environment and the demand for real-time target recognition, this paper proposes an image recognition method based on the combination of EfficientDet and Generative Adversarial Network (GAN), in which the gauged image features extracted from the EfficientDet model are used as the input of the GAN for the game learning of image categories and features. The learning results are also used as the feature reuse input of the EfficientDet for feature learning, so that this recognition model can obtain higher recognition speed and recognition accuracy. Through model testing with embedded experiments on unmanned platforms, the results show that the network has a higher mean-Average-Precision compared to the traditional one-stage approach, while improving the recognition accuracy of targets in different complex environments while maintaining no significant reduction in Frames Per Second.