Abstract
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.
Highlights
In terms of military applications, compared with intelligent object recognition tests in real scenes, artificial near-real virtual scenes have more significant advantages on cost, fidelity, repetition rate, and controllability
In order to meet the technical requirements of the intelligent development of auxiliary combat systems, we constructed a visual intelligent test platform
Inspired by many style transfer approaches based on GAN [1–4], we proposed an algorithm, which included a generator to render natural military scene images in different weather in the same camera view based on CycleGAN [5], so as to provide specific datasets
Summary
In terms of military applications, compared with intelligent object recognition tests in real scenes, artificial near-real virtual scenes have more significant advantages on cost, fidelity, repetition rate, and controllability. We get the large-scale images of the virtual scene in different environment based on the physics engine. For an unmodeled single real military scene image, the generator of our style transfer algorithm can render it. Based on the intelligent platform, we can measure and evaluate an intelligent detection algorithm in multiple conditions such as different illumination, environment, scale, and angle. On the platform of near-real virtual scene, we can measure image detection methods from illumination, environment, perspective, scale adaptability, and other aspects to obtain the performance score and speed score under different conditions. For intelligent algorithms on different missions, after scoring on all four indicators, we added a comprehensive evaluation model based on the analytic hierarchy process [16]. Combining the weight of the judgment matrix, the comprehensive evaluation score of the algorithm was obtained
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