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According to the technical requirements of intelligent development of auxiliary combat system, we construct a visual intelligent test platform. A near-real military scene dataset based on physical rendering is built, which contains 11,000 remote sensing images collected by an analog camera taking pictures in different illumination, weather environment, camera shooting angle, and scene scale condition. Besides, we add a natural style transfer module for a single unmodeled military scene image’s multienvironment generation. We conduct experiments to evaluate the stability of several UAV remote sensing image object detection algorithms. Based on the quality and speed value of the tested algorithms, the adaptability scores in different environments are calculated. Furthermore, we propose a comprehensive evaluation index system of military remote object detection based on a hierarchical model. We envision that our comprehensive benchmark will play a role in the evaluation of algorithm capability for military object detection tasks and the improvement of training algorithm capability.

Benchmarking the Robustness of Object Detection Based on Near-Real Military Scenes